diff --git a/tests/aftu/graph_compare_utils.py b/tests/aftu/graph_compare_utils.py
index 44f8384e4..6a22ae8a0 100644
--- a/tests/aftu/graph_compare_utils.py
+++ b/tests/aftu/graph_compare_utils.py
@@ -9,6 +9,7 @@
 from subprocess import PIPE, STDOUT, CalledProcessError, TimeoutExpired, run
 from typing import Optional
 
+from spyre_util import ModelInfo
 from vllm.model_executor.model_loader.weight_utils import (
     download_weights_from_hf)
 
@@ -157,14 +158,13 @@ def run_inference_py_and_get_graphs(
     return aftu_graphs
 
 
-def get_model_path(model_name_or_path):
-    is_local = os.path.isdir(model_name_or_path)
-    model_path = model_name_or_path
-    # Get location of model from HF cache.
-    if not is_local:
-        model_path = download_weights_from_hf(
-            model_name_or_path=model_path,
-            cache_dir=None,
-            allow_patterns=["*.safetensors", "*.bin", "*.pt"])
+def get_model_path(model: ModelInfo):
+    if os.path.isdir(model.name):
+        return model.name
 
-    return model_path
+    # Get location of model from HF cache.
+    return download_weights_from_hf(
+        model_name_or_path=model.name,
+        cache_dir=None,
+        allow_patterns=["*.safetensors", "*.bin", "*.pt"],
+        revision=model.revision)
diff --git a/tests/aftu/test_compare_graphs.py b/tests/aftu/test_compare_graphs.py
index 437a0e36a..3baa04f6d 100644
--- a/tests/aftu/test_compare_graphs.py
+++ b/tests/aftu/test_compare_graphs.py
@@ -12,13 +12,13 @@
                                  get_aftu_script_dir, get_model_path,
                                  run_inference_py_and_get_graphs)
 from output_util import generate_spyre_vllm_output
-from spyre_util import DecodeWarmupShapes, get_chicken_soup_prompts
+from spyre_util import DecodeWarmupShapes, ModelInfo, get_chicken_soup_prompts
 from vllm import SamplingParams
 
 
 @pytest.mark.spyre
 @pytest.mark.cb
-def test_compare_graphs_cb(model: str, max_num_seqs: int,
+def test_compare_graphs_cb(model: ModelInfo, max_num_seqs: int,
                            monkeypatch: pytest.MonkeyPatch, use_llm_cache):
     """Test that the spyre worker correctly outputs
     continuous batches of requests by comparing to HF"""
@@ -43,7 +43,8 @@ def test_compare_graphs_cb(model: str, max_num_seqs: int,
 
     extra_env = {
         "VLLM_DT_MAX_CONTEXT_LEN": str(max_model_len),
-        "VLLM_DT_MAX_BATCH_SIZE": str(max_num_seqs)
+        "VLLM_DT_MAX_BATCH_SIZE": str(max_num_seqs),
+        "VLLM_DT_MAX_BATCH_TKV_LIMIT": str(1024 * 128)
     }
     aftu_graphs = run_inference_py_and_get_graphs(inference_py_args,
                                                   script_dir, extra_env)
@@ -91,7 +92,7 @@ def test_compare_graphs_cb(model: str, max_num_seqs: int,
 @pytest.mark.parametrize(
     "warmup_shapes", [[(64, 4, 4)]])  # (prompt_length/new_tokens/batch_size)
 def test_compare_graphs_static_batching(
-        model: str, warmup_shapes: DecodeWarmupShapes,
+        model: ModelInfo, warmup_shapes: DecodeWarmupShapes,
         monkeypatch: pytest.MonkeyPatch) -> None:
 
     # AFTU
diff --git a/tests/e2e/test_spyre_async_llm.py b/tests/e2e/test_spyre_async_llm.py
index de4184549..1e977ab53 100644
--- a/tests/e2e/test_spyre_async_llm.py
+++ b/tests/e2e/test_spyre_async_llm.py
@@ -2,7 +2,7 @@
 from contextlib import ExitStack
 
 import pytest
-from spyre_util import DecodeWarmupShapes, get_chicken_soup_prompts
+from spyre_util import DecodeWarmupShapes, ModelInfo, get_chicken_soup_prompts
 from vllm import PromptType, SamplingParams
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -47,7 +47,7 @@ async def generate(
 @pytest.mark.parametrize(
     "output_kind", [RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY])
 @pytest.mark.asyncio
-async def test_abort(model: str, backend: str, cb: int,
+async def test_abort(model: ModelInfo, backend: str, cb: int,
                      warmup_shapes: DecodeWarmupShapes,
                      output_kind: RequestOutputKind,
                      monkeypatch: pytest.MonkeyPatch):
@@ -70,12 +70,11 @@ async def test_abort(model: str, backend: str, cb: int,
 
         # Async LLM API is a little different between v0 and V1
         engine = AsyncLLM.from_engine_args(
-            AsyncEngineArgs(
-                model=model,
-                tokenizer=model,
-                max_model_len=128,
-                max_num_seqs=4,
-            ))
+            AsyncEngineArgs(model=model.name,
+                            tokenizer=model.name,
+                            max_model_len=256,
+                            max_num_seqs=4,
+                            revision=model.revision))
         has_unfinished_requests = \
             engine.output_processor.has_unfinished_requests
         after.callback(engine.shutdown)
diff --git a/tests/e2e/test_spyre_basic.py b/tests/e2e/test_spyre_basic.py
index b641b721d..18afdae95 100644
--- a/tests/e2e/test_spyre_basic.py
+++ b/tests/e2e/test_spyre_basic.py
@@ -5,7 +5,7 @@
 
 import pytest
 from output_util import check_output_against_hf, generate_spyre_vllm_output
-from spyre_util import (DecodeWarmupShapes, create_random_request,
+from spyre_util import (DecodeWarmupShapes, ModelInfo, create_random_request,
                         get_chicken_soup_prompts, patch_environment,
                         skip_unsupported_tp_size)
 from vllm import EngineArgs, SamplingParams
@@ -16,7 +16,7 @@
 
 
 @pytest.mark.full_model
-def test_output(model: str, tp_size: int, backend: str, cb: int,
+def test_output(model: ModelInfo, tp_size: int, backend: str, cb: int,
                 max_num_seqs: int, max_model_len: int,
                 warmup_shapes: DecodeWarmupShapes,
                 monkeypatch: pytest.MonkeyPatch, use_llm_cache) -> None:
@@ -70,8 +70,9 @@ def test_output(model: str, tp_size: int, backend: str, cb: int,
     pytest.param(
         "sendnn_decoder", marks=pytest.mark.spyre, id="sendnn_decoder")
 ])
-def test_output_sendnn_decoder(model: str, warmup_shapes: DecodeWarmupShapes,
-                               backend: str, monkeypatch: pytest.MonkeyPatch,
+def test_output_sendnn_decoder(model: ModelInfo,
+                               warmup_shapes: DecodeWarmupShapes, backend: str,
+                               monkeypatch: pytest.MonkeyPatch,
                                use_llm_cache) -> None:
     '''
     Tests the deprecated sendnn_decoder backend, which should fall-back to
@@ -101,7 +102,7 @@ def test_output_sendnn_decoder(model: str, warmup_shapes: DecodeWarmupShapes,
                             prompts)
 
 
-def test_batch_handling(model: str, backend: str, cb: int, warmup_shapes,
+def test_batch_handling(model: ModelInfo, backend: str, cb: int, warmup_shapes,
                         max_num_seqs: int, max_model_len: int,
                         monkeypatch: pytest.MonkeyPatch, use_llm_cache):
     """Test that the spyre worker correctly handles
@@ -147,7 +148,7 @@ def test_batch_handling(model: str, backend: str, cb: int, warmup_shapes,
                             prompts)
 
 
-def test_full_batch_scheduling(model: str, backend: str, monkeypatch):
+def test_full_batch_scheduling(model: ModelInfo, backend: str, monkeypatch):
     """Test that we can schedule a full batch of prompts."""
 
     # We need to ensure here that the max number of tokens in a full batch
@@ -171,10 +172,11 @@ def test_full_batch_scheduling(model: str, backend: str, monkeypatch):
     monkeypatch.setenv("VLLM_SPYRE_DYNAMO_BACKEND", backend)
 
     # Setup the engine
-    engine_args = EngineArgs(model=model,
-                             tokenizer=model,
+    engine_args = EngineArgs(model=model.name,
+                             tokenizer=model.name,
                              max_num_batched_tokens=max_batched_tokens,
-                             max_num_seqs=batch_size)
+                             max_num_seqs=batch_size,
+                             revision=model.revision)
     vllm_config = engine_args.create_engine_config()
     executor_class = Executor.get_class(vllm_config)
     engine_core = EngineCore(vllm_config=vllm_config,
@@ -191,14 +193,14 @@ def test_full_batch_scheduling(model: str, backend: str, monkeypatch):
                 request_id=i,
                 num_tokens=max_batched_tokens,
                 sampling_params=vllm_sampling_params,
-                model=model,
+                model=model.name,
             ))
     schedule = scheduler.schedule()
 
     assert len(schedule.scheduled_new_reqs) == batch_size
 
 
-def test_max_model_len_override(model: str, backend, warmup_shapes, cb,
+def test_max_model_len_override(model: ModelInfo, backend, warmup_shapes, cb,
                                 monkeypatch):
     """Test that makes sure that --max-model-len
     doesn't affect SB, instead it is picked up from
@@ -215,7 +217,7 @@ def test_max_model_len_override(model: str, backend, warmup_shapes, cb,
 
     patch_environment(**kwargs, backend=backend, monkeypatch=monkeypatch)
     vllm_config = EngineArgs(
-        model=model, max_model_len=max_model_len).create_engine_config()
+        model=model.name, max_model_len=max_model_len).create_engine_config()
     model_config = vllm_config.model_config
 
     if not cb:
diff --git a/tests/e2e/test_spyre_cb.py b/tests/e2e/test_spyre_cb.py
index 3b1908044..d679906e6 100644
--- a/tests/e2e/test_spyre_cb.py
+++ b/tests/e2e/test_spyre_cb.py
@@ -12,7 +12,7 @@
 from openai import BadRequestError
 from output_util import (check_output_against_hf, compare_results,
                          extract_output, generate_spyre_vllm_output)
-from spyre_util import (RemoteOpenAIServer, create_seq_prompt,
+from spyre_util import (ModelInfo, RemoteOpenAIServer, create_seq_prompt,
                         get_chicken_soup_prompts, skip_unsupported_tp_size)
 from vllm import LLM, SamplingParams
 
@@ -20,7 +20,7 @@
 @pytest.mark.cb
 @pytest.mark.parametrize(
     "backend", [pytest.param("eager", marks=pytest.mark.cpu, id="eager")])
-def test_cb_max_tokens(model: str, backend: str, max_model_len: int,
+def test_cb_max_tokens(model: ModelInfo, backend: str, max_model_len: int,
                        max_num_seqs: int, monkeypatch: pytest.MonkeyPatch,
                        use_llm_cache):
     """Test that continuous batches of requests that
@@ -52,7 +52,7 @@ def test_cb_max_tokens(model: str, backend: str, max_model_len: int,
     "backend", [pytest.param("eager", marks=pytest.mark.cpu, id="eager")])
 def test_api_cb_rejects_oversized_request(
     remote_openai_server: RemoteOpenAIServer,
-    model: str,
+    model: ModelInfo,
     backend: str,
     cb: bool,
     max_model_len: int,
@@ -67,7 +67,7 @@ def test_api_cb_rejects_oversized_request(
     with pytest.raises(BadRequestError,
                        match="This model's maximum context length is"):
         client.completions.create(
-            model=model,
+            model=model.name,
             prompt=overflow_prompt,
             max_tokens=max_tokens,
         )
@@ -79,7 +79,7 @@ def test_api_cb_rejects_oversized_request(
     "backend", [pytest.param("eager", marks=pytest.mark.cpu, id="eager")])
 def test_api_cb_generates_correct_max_tokens(
     remote_openai_server: RemoteOpenAIServer,
-    model: str,
+    model: ModelInfo,
     backend: str,
     cb: bool,
     max_model_len: int,
@@ -90,7 +90,7 @@ def test_api_cb_generates_correct_max_tokens(
     client = remote_openai_server.get_client()
     max_tokens = 10
 
-    response = client.completions.create(model=model,
+    response = client.completions.create(model=model.name,
                                          prompt=get_chicken_soup_prompts(1),
                                          max_tokens=max_tokens,
                                          temperature=0)
@@ -110,7 +110,7 @@ def test_api_cb_generates_correct_max_tokens(
     ids=lambda val: f"TP({val})",
 )
 def test_long_context_batches(
-    model: str,
+    model: ModelInfo,
     backend: str,
     tp_size: int,
     monkeypatch: pytest.MonkeyPatch,
@@ -136,13 +136,12 @@ def test_long_context_batches(
         (2, 9000),
     ]
 
-    vllm_model = LLM(
-        model=model,
-        tokenizer=model,
-        max_model_len=max_model_len,
-        max_num_seqs=max_num_seqs,
-        tensor_parallel_size=tp_size,
-    )
+    vllm_model = LLM(model=model.name,
+                     tokenizer=model.name,
+                     max_model_len=max_model_len,
+                     max_num_seqs=max_num_seqs,
+                     tensor_parallel_size=tp_size,
+                     revision=model.revision)
 
     sampling_params = SamplingParams(
         max_tokens=max_tokens,
@@ -152,7 +151,7 @@ def test_long_context_batches(
     )
 
     for batch_size, token_len in batch_token_pairs:
-        prompt = create_seq_prompt(model, token_length=token_len)
+        prompt = create_seq_prompt(model.name, token_length=token_len)
         prompts = [prompt] * batch_size
 
         vllm_outputs = vllm_model.generate(prompts, sampling_params)
diff --git a/tests/e2e/test_spyre_cb_scheduler_steps.py b/tests/e2e/test_spyre_cb_scheduler_steps.py
index e5e357f7e..d596f2ef8 100644
--- a/tests/e2e/test_spyre_cb_scheduler_steps.py
+++ b/tests/e2e/test_spyre_cb_scheduler_steps.py
@@ -9,15 +9,16 @@
 import pytest
 from output_util import check_output_against_hf
 from scheduling_utils import check_scheduler_inference_steps
+from spyre_util import ModelInfo
 
 
 @pytest.mark.cb
 @pytest.mark.full_model
 # These values are all parameterized for test sorting
 @pytest.mark.parametrize("max_num_seqs", [2])
-@pytest.mark.parametrize("max_model_len", [192])
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
-def test_prompts_aligned_with_tkv_boundaries(model: str, backend: str,
+@pytest.mark.parametrize("max_model_len", [256])
+@pytest.mark.parametrize("available_blocks", [None])
+def test_prompts_aligned_with_tkv_boundaries(model: ModelInfo, backend: str,
                                              monkeypatch: pytest.MonkeyPatch,
                                              set_random_seed: None,
                                              max_num_seqs: int,
@@ -176,10 +177,10 @@ def test_prompts_aligned_with_tkv_boundaries(model: str, backend: str,
 @pytest.mark.full_model
 # These values are all parameterized for test sorting
 @pytest.mark.parametrize("max_num_seqs", [2])
-@pytest.mark.parametrize("max_model_len", [128])
-@pytest.mark.parametrize("available_blocks", [8])  # no restriction
+@pytest.mark.parametrize("max_model_len", [256])
+@pytest.mark.parametrize("available_blocks", [None])
 def test_prompts_misaligned_with_tkv_boundaries(
-        model: str, backend: str, monkeypatch: pytest.MonkeyPatch,
+        model: ModelInfo, backend: str, monkeypatch: pytest.MonkeyPatch,
         set_random_seed: None, max_num_seqs: int, max_model_len: int,
         available_blocks: int):
     """ Scenario where it happens that some sequence gets scheduled in a way 
@@ -334,9 +335,9 @@ def test_prompts_misaligned_with_tkv_boundaries(
 # These values are all parameterized for test sorting
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [128])
-@pytest.mark.parametrize("available_blocks", [8])  # no restriction
+@pytest.mark.parametrize("available_blocks", [None])
 def test_two_sequences_finish_same_time_as_new_arrive(
-        model: str, backend: str, monkeypatch: pytest.MonkeyPatch,
+        model: ModelInfo, backend: str, monkeypatch: pytest.MonkeyPatch,
         set_random_seed, max_num_seqs: int, max_model_len: int,
         available_blocks: int):
     """ 2-cases-in-1: (1) Two sequences finish at the same time and (2) a new
@@ -480,8 +481,10 @@ def test_two_sequences_finish_same_time_as_new_arrive(
 # These values are all parameterized for test sorting
 @pytest.mark.parametrize("max_num_seqs", [3])
 @pytest.mark.parametrize("max_model_len", [192])
-@pytest.mark.parametrize("available_blocks", [18])  # no restriction
-def test_new_sequence_joins_during_decode(model: str, backend: str,
+@pytest.mark.parametrize(
+    "available_blocks",
+    [12])  # specific value required to pass compilation with this config
+def test_new_sequence_joins_during_decode(model: ModelInfo, backend: str,
                                           monkeypatch: pytest.MonkeyPatch,
                                           set_random_seed, max_num_seqs: int,
                                           max_model_len: int,
@@ -664,8 +667,8 @@ def test_new_sequence_joins_during_decode(model: str, backend: str,
 # These values are all parameterized for test sorting
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [192])
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
-def test_prompt_too_long_for_current_tkv(model: str, backend: str,
+@pytest.mark.parametrize("available_blocks", [None])
+def test_prompt_too_long_for_current_tkv(model: ModelInfo, backend: str,
                                          prefill_optimization: bool,
                                          monkeypatch: pytest.MonkeyPatch,
                                          set_random_seed, max_num_seqs: int,
@@ -887,8 +890,8 @@ def test_prompt_too_long_for_current_tkv(model: str, backend: str,
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len",
                          [192])  # restricted to violate scheduler condition
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
-def test_prefill_optimization_tkv_too_big(model: str, backend: str,
+@pytest.mark.parametrize("available_blocks", [None])
+def test_prefill_optimization_tkv_too_big(model: ModelInfo, backend: str,
                                           monkeypatch: pytest.MonkeyPatch,
                                           set_random_seed, max_num_seqs: int,
                                           max_model_len: int,
@@ -1059,7 +1062,7 @@ def test_prefill_optimization_tkv_too_big(model: str, backend: str,
 # at least 5 blocks would be required
 @pytest.mark.parametrize("available_blocks", [4])
 def test_prefill_optimization_use_more_than_available_blocks(
-        model: str, backend: str, monkeypatch: pytest.MonkeyPatch,
+        model: ModelInfo, backend: str, monkeypatch: pytest.MonkeyPatch,
         set_random_seed, max_num_seqs: int, max_model_len: int,
         available_blocks: int):
     """ Scenario where the requested prompt is too long for current tkv value
@@ -1193,9 +1196,9 @@ def test_prefill_optimization_use_more_than_available_blocks(
 # These values are all parameterized for test sorting
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [128])
-@pytest.mark.parametrize("available_blocks", [8])  # no restriction
+@pytest.mark.parametrize("available_blocks", [None])
 def test_requested_tokens_not_fitting_remaining_space(
-        model: str, backend: str, monkeypatch: pytest.MonkeyPatch,
+        model: ModelInfo, backend: str, monkeypatch: pytest.MonkeyPatch,
         set_random_seed, max_num_seqs: int, max_model_len: int,
         available_blocks: int):
     """ Scenario where the request goes beyond max_model_len and needs to wait
@@ -1364,7 +1367,7 @@ def test_requested_tokens_not_fitting_remaining_space(
 @pytest.mark.parametrize("max_num_seqs", [4])
 @pytest.mark.parametrize("max_model_len", [128])
 @pytest.mark.parametrize("available_blocks", [8])
-def test_requests_use_all_available_blocks(model: str, backend: str,
+def test_requests_use_all_available_blocks(model: ModelInfo, backend: str,
                                            monkeypatch: pytest.MonkeyPatch,
                                            set_random_seed, max_num_seqs: int,
                                            max_model_len: int,
@@ -1504,7 +1507,7 @@ def test_requests_use_all_available_blocks(model: str, backend: str,
 @pytest.mark.parametrize("max_model_len", [128])
 @pytest.mark.parametrize("available_blocks", [4])
 def test_requests_use_more_than_available_blocks(
-        model: str, backend: str, monkeypatch: pytest.MonkeyPatch,
+        model: ModelInfo, backend: str, monkeypatch: pytest.MonkeyPatch,
         set_random_seed, max_num_seqs: int, max_model_len: int,
         available_blocks: int):
     """ Scenario where some request need to wait because of the number of 
@@ -1666,8 +1669,8 @@ def test_requests_use_more_than_available_blocks(
 @pytest.mark.full_model
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [192])
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
-def test_requests_use_full_batch_tkv_limit(model: str, backend: str,
+@pytest.mark.parametrize("available_blocks", [None])
+def test_requests_use_full_batch_tkv_limit(model: ModelInfo, backend: str,
                                            monkeypatch: pytest.MonkeyPatch,
                                            set_random_seed, max_num_seqs: int,
                                            max_model_len: int,
@@ -1797,8 +1800,8 @@ def test_requests_use_full_batch_tkv_limit(model: str, backend: str,
 @pytest.mark.full_model
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [192])
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
-def test_requests_exceed_batch_tkv_limit(model: str, backend: str,
+@pytest.mark.parametrize("available_blocks", [None])
+def test_requests_exceed_batch_tkv_limit(model: ModelInfo, backend: str,
                                          monkeypatch: pytest.MonkeyPatch,
                                          set_random_seed, max_num_seqs: int,
                                          max_model_len: int,
@@ -1938,9 +1941,9 @@ def test_requests_exceed_batch_tkv_limit(model: str, backend: str,
 @pytest.mark.full_model
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [192])
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
+@pytest.mark.parametrize("available_blocks", [None])
 def test_requests_use_full_batch_tkv_limit_prefill_opt(
-        model: str, backend: str, monkeypatch: pytest.MonkeyPatch,
+        model: ModelInfo, backend: str, monkeypatch: pytest.MonkeyPatch,
         set_random_seed, max_num_seqs: int, max_model_len: int,
         available_blocks: int):
     """ Scenario where all requests can be scheduled right away as the
@@ -2049,9 +2052,9 @@ def test_requests_use_full_batch_tkv_limit_prefill_opt(
 @pytest.mark.full_model
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [192])
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
+@pytest.mark.parametrize("available_blocks", [None])
 def test_requests_exceed_batch_tkv_limit_prefill_opt(
-        model: str, backend: str, monkeypatch: pytest.MonkeyPatch,
+        model: ModelInfo, backend: str, monkeypatch: pytest.MonkeyPatch,
         set_random_seed, max_num_seqs: int, max_model_len: int,
         available_blocks: int):
     """ Scenario where a request cannot be scheduled right away as the
@@ -2173,9 +2176,9 @@ def test_requests_exceed_batch_tkv_limit_prefill_opt(
 # These values are all parameterized for test sorting
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [128])
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
+@pytest.mark.parametrize("available_blocks", [None])
 def test_scheduler_heuristic_prioritize_prefill(
-        model: str, backend: str, monkeypatch: pytest.MonkeyPatch,
+        model: ModelInfo, backend: str, monkeypatch: pytest.MonkeyPatch,
         set_random_seed, max_num_seqs: int, max_model_len: int,
         available_blocks: int):
     """ Scenario where the prefill is prioritized over the decode as the
@@ -2290,8 +2293,8 @@ def test_scheduler_heuristic_prioritize_prefill(
 # These values are all parameterized for test sorting
 @pytest.mark.parametrize("max_num_seqs", [2])
 @pytest.mark.parametrize("max_model_len", [192])
-@pytest.mark.parametrize("available_blocks", [16])  # no restriction
-def test_scheduler_heuristic_prioritize_decode(model: str, backend: str,
+@pytest.mark.parametrize("available_blocks", [None])
+def test_scheduler_heuristic_prioritize_decode(model: ModelInfo, backend: str,
                                                monkeypatch: pytest.MonkeyPatch,
                                                set_random_seed,
                                                max_num_seqs: int,
diff --git a/tests/e2e/test_spyre_embeddings.py b/tests/e2e/test_spyre_embeddings.py
index 918a40a6a..93050dfcf 100644
--- a/tests/e2e/test_spyre_embeddings.py
+++ b/tests/e2e/test_spyre_embeddings.py
@@ -8,8 +8,9 @@
 import pytest
 from output_util import (compare_embedding_results, spyre_vllm_embeddings,
                          st_embeddings)
-from spyre_util import (EmbeddingWarmupShapes, get_chicken_soup_prompts,
-                        get_spyre_model_list, patch_warmup_shapes)
+from spyre_util import (EmbeddingWarmupShapes, ModelInfo,
+                        get_chicken_soup_prompts, get_spyre_model_list,
+                        patch_warmup_shapes)
 from vllm import LLM
 
 
@@ -23,7 +24,7 @@
         pytest.param([(128, 8)])
     ])
 def test_output(
-    model: str,
+    model: ModelInfo,
     warmup_shapes: EmbeddingWarmupShapes,
     backend: str,
     monkeypatch,
@@ -64,7 +65,7 @@ def test_output(
 ])  # (prompt_length/batch_size)
 @pytest.mark.parametrize("model", get_spyre_model_list(isEmbeddings=True))
 def test_scheduling_invariance(
-    model,
+    model: ModelInfo,
     backend,
     warmup_shapes: EmbeddingWarmupShapes,
     monkeypatch,
@@ -84,11 +85,12 @@ def test_scheduling_invariance(
     prompts = get_chicken_soup_prompts(4)
     reference_embeds = st_embeddings(model, prompts)
 
-    vllm_model = LLM(model=model,
+    vllm_model = LLM(model=model.name,
                      task="embed",
-                     tokenizer=model,
+                     tokenizer=model.name,
                      max_model_len=256,
-                     tensor_parallel_size=1)
+                     tensor_parallel_size=1,
+                     revision=model.revision)
 
     def batch_embeds(step):
         vllm_outputs = []
diff --git a/tests/e2e/test_spyre_max_new_tokens.py b/tests/e2e/test_spyre_max_new_tokens.py
index deb7fdce0..48278c90f 100644
--- a/tests/e2e/test_spyre_max_new_tokens.py
+++ b/tests/e2e/test_spyre_max_new_tokens.py
@@ -5,12 +5,12 @@
 
 import pytest
 from output_util import check_output_against_hf, generate_spyre_vllm_output
-from spyre_util import DecodeWarmupShapes, get_chicken_soup_prompts
+from spyre_util import DecodeWarmupShapes, ModelInfo, get_chicken_soup_prompts
 from vllm import SamplingParams
 
 
 @pytest.mark.parametrize("stop_last", [True, False])
-def test_output(model: str, stop_last: bool, max_model_len: int,
+def test_output(model: ModelInfo, stop_last: bool, max_model_len: int,
                 max_num_seqs: int, warmup_shapes: DecodeWarmupShapes,
                 backend: str, cb: int, monkeypatch: pytest.MonkeyPatch,
                 use_llm_cache) -> None:
diff --git a/tests/e2e/test_spyre_online.py b/tests/e2e/test_spyre_online.py
index ff14355e1..2b4c7d204 100644
--- a/tests/e2e/test_spyre_online.py
+++ b/tests/e2e/test_spyre_online.py
@@ -27,6 +27,7 @@ def test_openai_serving(
     """Test online serving using the `vllm serve` CLI"""
 
     client = remote_openai_server.get_client()
+    model = model.name
 
     _check_result(client, model, n=1)
     _check_result(client, model, temperature=1.0, n=2)
diff --git a/tests/e2e/test_spyre_prompt_logprobs.py b/tests/e2e/test_spyre_prompt_logprobs.py
index 1479f79bc..b76cbb0a4 100644
--- a/tests/e2e/test_spyre_prompt_logprobs.py
+++ b/tests/e2e/test_spyre_prompt_logprobs.py
@@ -73,7 +73,7 @@ def test_prompt_logprobs_not_supported_with_cb(
     monkeypatch.setenv("VLLM_SPYRE_USE_CB", "1")
 
     with pytest.raises(ValueError, match="continuous batching"):
-        VllmConfig(model_config=ModelConfig(model=model, task="generate"))
+        VllmConfig(model_config=ModelConfig(model=model.name, task="generate"))
 
 
 @pytest.mark.cpu
@@ -85,7 +85,7 @@ def test_prompt_logprobs_on_single_requests_only(
     monkeypatch.setenv("VLLM_SPYRE_WARMUP_BATCH_SIZES", "2")
 
     with pytest.raises(ValueError, match="batch size 1"):
-        VllmConfig(model_config=ModelConfig(model=model, task="generate"))
+        VllmConfig(model_config=ModelConfig(model=model.name, task="generate"))
 
 
 def _compare_prompt_logprobs(expected: list, actual: list,
diff --git a/tests/e2e/test_spyre_scoring.py b/tests/e2e/test_spyre_scoring.py
index 1c340b476..ffa62fb4b 100644
--- a/tests/e2e/test_spyre_scoring.py
+++ b/tests/e2e/test_spyre_scoring.py
@@ -27,7 +27,7 @@ def test_serving(remote_openai_server, model, warmup_shapes, backend):
                                      "text_2": [docs[0], docs[1]]
                                  }).json()
 
-    ce_model = CrossEncoder(model)
+    ce_model = CrossEncoder(model.name, revision=model.revision)
     ce_scores = ce_model.predict([(query, docs[0]), (query, docs[1])])
 
     vllm_scores = [o["score"] for o in vllm_outputs["data"]]
diff --git a/tests/e2e/test_spyre_seed.py b/tests/e2e/test_spyre_seed.py
index 395cf216c..9e2fdc76f 100644
--- a/tests/e2e/test_spyre_seed.py
+++ b/tests/e2e/test_spyre_seed.py
@@ -7,16 +7,16 @@
 
 import pytest
 from output_util import generate_spyre_vllm_output
-from spyre_util import DecodeWarmupShapes, get_chicken_soup_prompts
+from spyre_util import DecodeWarmupShapes, ModelInfo, get_chicken_soup_prompts
 from vllm import SamplingParams
 
 
 @pytest.mark.parametrize("temperature", [0.1, 1.0])
 @pytest.mark.parametrize("seed", [42])
-def test_seed(model: str, temperature: float, seed: int, max_model_len: int,
-              max_num_seqs: int, warmup_shapes: DecodeWarmupShapes,
-              backend: str, cb: int, monkeypatch: pytest.MonkeyPatch,
-              use_llm_cache) -> None:
+def test_seed(model: ModelInfo, temperature: float, seed: int,
+              max_model_len: int, max_num_seqs: int,
+              warmup_shapes: DecodeWarmupShapes, backend: str, cb: int,
+              monkeypatch: pytest.MonkeyPatch, use_llm_cache) -> None:
     '''
     The warmup is based on a single shape. After the warmup,
     output is generated for one request with 5 identical prompts
diff --git a/tests/e2e/test_spyre_stagger_basic.py b/tests/e2e/test_spyre_stagger_basic.py
index bc5e82f19..422d53c1e 100644
--- a/tests/e2e/test_spyre_stagger_basic.py
+++ b/tests/e2e/test_spyre_stagger_basic.py
@@ -6,17 +6,18 @@
 
 import pytest
 from output_util import check_output_against_hf, generate_spyre_vllm_output
-from spyre_util import get_chicken_soup_prompts, skip_unsupported_tp_size
+from spyre_util import (ModelInfo, get_chicken_soup_prompts,
+                        skip_unsupported_tp_size)
 from vllm import SamplingParams
 
 
-def test_stagger_output(model: str, tp_size: int, backend: str, cb: int,
+def test_stagger_output(model: ModelInfo, tp_size: int, backend: str, cb: int,
                         max_num_seqs: int, max_model_len: int, warmup_shapes,
                         monkeypatch: pytest.MonkeyPatch,
                         use_llm_cache) -> None:
     '''
     This test verifies that generated output is still correct
-    when stagget mode is enabled.
+    when stagger mode is enabled.
     VLLM_SPYRE_MAX_LOAD_PROCESSES is set to 1, allowing
     only a single worker to load or compile the model at
     a time.
diff --git a/tests/e2e/test_spyre_static_batching_limits.py b/tests/e2e/test_spyre_static_batching_limits.py
index 5359f8582..4c7f2de45 100644
--- a/tests/e2e/test_spyre_static_batching_limits.py
+++ b/tests/e2e/test_spyre_static_batching_limits.py
@@ -5,7 +5,7 @@
 
 import pytest
 from llm_cache import get_cached_llm
-from spyre_util import DecodeWarmupShapes, create_text_prompt
+from spyre_util import DecodeWarmupShapes, ModelInfo, create_text_prompt
 from vllm import SamplingParams
 
 
@@ -13,7 +13,7 @@
     "warmup_shapes",
     [[(64, 20, 4)], [(64, 20, 4),
                      (128, 20, 2)]])  # (prompt_length/new_tokens/batch_size)
-def test_max_prompt_len_and_new_tokens(model: str,
+def test_max_prompt_len_and_new_tokens(model: ModelInfo,
                                        warmup_shapes: DecodeWarmupShapes,
                                        backend: str, use_llm_cache,
                                        monkeypatch) -> None:
@@ -45,7 +45,7 @@ def test_max_prompt_len_and_new_tokens(model: str,
 
     # Craft a request with a prompt that is slightly too long for the warmup
     # shape
-    prompt = create_text_prompt(model,
+    prompt = create_text_prompt(model.name,
                                 min_token_length=max_prompt_length,
                                 max_token_length=max_prompt_length +
                                 max_new_tokens - 1)
diff --git a/tests/e2e/test_spyre_warmup_shapes.py b/tests/e2e/test_spyre_warmup_shapes.py
index 334f55b9e..1f0bbdc39 100644
--- a/tests/e2e/test_spyre_warmup_shapes.py
+++ b/tests/e2e/test_spyre_warmup_shapes.py
@@ -5,14 +5,15 @@
 
 import pytest
 from output_util import check_output_against_hf, generate_spyre_vllm_output
-from spyre_util import DecodeWarmupShapes, get_chicken_soup_prompts
+from spyre_util import DecodeWarmupShapes, ModelInfo, get_chicken_soup_prompts
 from vllm import SamplingParams
 
 
 @pytest.mark.parametrize(
     "warmup_shapes", [[(64, 20, 4),
                        (128, 20, 2)]])  # (prompt_length/new_tokens/batch_size)
-def test_multiple_warmup_shapes(model: str, warmup_shapes: DecodeWarmupShapes,
+def test_multiple_warmup_shapes(model: ModelInfo,
+                                warmup_shapes: DecodeWarmupShapes,
                                 backend: str, monkeypatch: pytest.MonkeyPatch,
                                 use_llm_cache) -> None:
     '''
@@ -56,7 +57,7 @@ def test_multiple_warmup_shapes(model: str, warmup_shapes: DecodeWarmupShapes,
 
 @pytest.mark.parametrize("prompts", [["Hello"]])
 @pytest.mark.parametrize("warmup_shapes", [[(65, 1, 1)]])
-def test_invalid_prompt_len(model: str, prompts: list[str],
+def test_invalid_prompt_len(model: ModelInfo, prompts: list[str],
                             warmup_shapes: DecodeWarmupShapes, backend: str,
                             monkeypatch: pytest.MonkeyPatch,
                             use_llm_cache) -> None:
diff --git a/tests/hf_cache.json b/tests/hf_cache.json
index 6ee28cddf..e3420f9f7 100644
--- a/tests/hf_cache.json
+++ b/tests/hf_cache.json
@@ -1,491 +1,611 @@
 {
     "ibm-ai-platform/micro-g3.3-8b-instruct-1b": {
-        "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623_540_565": {
-            "65": {
-                "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with r",
-                "token_ids": [ 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540 ],
-                "tokens": [ ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r" ],
-                "logprobs": [ -1.1383615732192993, -1.348477840423584, -0.6464436054229736, -1.0950212478637695, -1.1007599830627441, -0.040145955979824066, -0.013641467317938805, -0.4686796963214874, -0.35953083634376526, -0.1965043991804123, -0.005137456580996513, -0.08900478482246399, -0.0903414785861969, -0.2820867598056793, -0.0029427579138427973, -0.1985229104757309, -0.014203174039721489, -0.00561410840600729, -0.15048108994960785, -0.005251897498965263, -0.01913083717226982, -0.00036054308293387294, -0.08758144080638885, -0.2880907952785492, -0.03847683221101761, -0.28287413716316223, -0.10426884889602661, -0.006487381178885698, -0.005978439934551716, -0.19189994037151337, -0.12549462914466858, -0.06457288563251495, -0.0027542298194020987, -0.026423173025250435, -0.05056668817996979, -0.10096432268619537, -0.0030662447679787874, -0.11945100128650665, -0.01040734350681305, -0.0029708081856369972, -0.06660475581884384, -0.0030891813803464174, -0.02153186872601509, -0.00034350217902101576, -0.0949283316731453, -0.16384539008140564, -0.02655876986682415, -0.18885542452335358, -0.036614395678043365, -0.0024129818193614483, -0.004053353797644377, -0.13651177287101746, -0.06032523512840271, -0.029588980600237846, -0.001731802592985332, -0.016770802438259125, -0.029040368273854256, -0.051940903067588806, -0.0031533539295196533, -0.10485414415597916, -0.007868957705795765, -0.0024201171472668648, -0.04198240116238594, -0.003298677271232009, -0.01304908748716116 ]
-            },
-            "57": {
-                "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for",
-                "token_ids": [ 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436 ],
-                "tokens": [ ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for" ],
-                "logprobs": [ -1.1383615732192993, -1.348477840423584, -0.6464436054229736, -1.0950212478637695, -1.1007599830627441, -0.040145955979824066, -0.013641467317938805, -0.4686796963214874, -0.35953083634376526, -0.1965043991804123, -0.005137456580996513, -0.08900478482246399, -0.0903414785861969, -0.2820867598056793, -0.0029427579138427973, -0.1985229104757309, -0.014203174039721489, -0.00561410840600729, -0.15048108994960785, -0.005251897498965263, -0.01913083717226982, -0.00036054308293387294, -0.08758144080638885, -0.2880907952785492, -0.03847683221101761, -0.28287413716316223, -0.10426884889602661, -0.006487381178885698, -0.005978439934551716, -0.19189994037151337, -0.12549462914466858, -0.06457288563251495, -0.0027542298194020987, -0.026423173025250435, -0.05056668817996979, -0.10096432268619537, -0.0030662447679787874, -0.11945100128650665, -0.01040734350681305, -0.0029708081856369972, -0.06660475581884384, -0.0030891813803464174, -0.02153186872601509, -0.00034350217902101576, -0.0949283316731453, -0.16384539008140564, -0.02655876986682415, -0.18885542452335358, -0.036614395678043365, -0.0024129818193614483, -0.004053353797644377, -0.13651177287101746, -0.06032523512840271, -0.029588980600237846, -0.001731802592985332, -0.016770802438259125, -0.029040368273854256 ]
-            },
-            "30": {
-                "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide",
-                "token_ids": [ 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 32, 203, 203, 1482, 21081, 44, 203, 15277 ],
-                "tokens": [ ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide" ],
-                "logprobs": [ -1.1383615732192993, -1.348477840423584, -0.6464436054229736, -1.0950212478637695, -1.1007599830627441, -0.040145955979824066, -0.013641467317938805, -0.4686796963214874, -0.35953083634376526, -0.1965043991804123, -0.005137456580996513, -0.08900478482246399, -0.0903414785861969, -0.2820867598056793, -0.0029427579138427973, -0.1985229104757309, -0.014203174039721489, -0.00561410840600729, -0.15048108994960785, -0.005251897498965263, -0.01913083717226982, -0.00036054308293387294, -0.08758144080638885, -0.2880907952785492, -0.03847683221101761, -0.28287413716316223, -0.10426884889602661, -0.006487381178885698, -0.005978439934551716, -0.19189994037151337 ]
-            },
-            "119": {
-                "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a",
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-                "logprobs": [ -0.044418081641197205, -0.8381218314170837, -1.6231225728988647, -0.32129156589508057, -1.4336494207382202, -0.609569251537323, -1.7651867866516113, -2.183410406112671, -0.06972545385360718, -0.01936611346900463, -0.5101970434188843, -0.3766481876373291, -0.3819814622402191, -0.004696528892964125, -0.09388517588376999 ]
-            },
-            "13": {
-                "text": "paring the response.\n\n### Instruction:\nProvide a list",
-                "token_ids": [ 26124, 322, 1789, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149 ],
-                "tokens": [ "paring", " the", " response", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list" ],
-                "logprobs": [ -0.044418081641197205, -0.8381218314170837, -1.6231225728988647, -0.32129156589508057, -1.4336494207382202, -0.609569251537323, -1.7651867866516113, -2.183410406112671, -0.06972545385360718, -0.01936611346900463, -0.5101970434188843, -0.3766481876373291, -0.3819814622402191 ]
-            }
-        },
-        "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623": {
-            "7": {
-                "text": " a list of instructions for preparing",
-                "token_ids": [ 312, 1149, 432, 9400, 436, 1406, 26124 ],
-                "tokens": [ " a", " list", " of", " instructions", " for", " pre", "paring" ],
-                "logprobs": [ -1.229694128036499, -1.0843069553375244, -0.008545498363673687, -0.9488708972930908, -0.7169874906539917, -1.7615206241607666, -0.00820551160722971 ]
-            },
-            "9": {
-                "text": " a list of instructions for preparing chicken",
-                "token_ids": [ 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217 ],
-                "tokens": [ " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken" ],
-                "logprobs": [ -1.229694128036499, -1.0843069553375244, -0.008545498363673687, -0.9488708972930908, -0.7169874906539917, -1.7615206241607666, -0.00820551160722971, -0.6533018350601196, -0.03221869468688965 ]
-            },
-            "4": {
-                "text": " a list of instructions",
-                "token_ids": [ 312, 1149, 432, 9400 ],
-                "tokens": [ " a", " list", " of", " instructions" ],
-                "logprobs": [ -1.229694128036499, -1.0843064785003662, -0.008545498363673687, -0.9488720893859863 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup.\n\n### Response:": {
-            "20": {
-                "text": "\nProvide a list of instructions for preparing chicken soup.\n\n### Instruction:\nProvide",
-                "token_ids": [ 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 32, 203, 203, 1482, 21081, 44, 203, 15277 ],
-                "tokens": [ "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide" ],
-                "logprobs": [ -0.03829614818096161, -0.8993099927902222, -0.269023060798645, -0.29307451844215393, -0.0057315765880048275, -0.15898968279361725, -0.12841321527957916, -0.28819355368614197, -0.0024942022282630205, -0.2027340531349182, -0.013996068388223648, -0.0030258367769420147, -0.08510798960924149, -0.3096599280834198, -0.04846445471048355, -0.3071613013744354, -1.3310167789459229, -0.026373596861958504, -0.010276027955114841, -0.25900062918663025 ]
-            },
-            "5": {
-                "text": "\nProvide a list of",
-                "token_ids": [ 203, 15277, 312, 1149, 432 ],
-                "tokens": [ "\n", "Provide", " a", " list", " of" ],
-                "logprobs": [ -0.03829614818096161, -0.8993099927902222, -0.269023060798645, -0.29307451844215393, -0.0057315765880048275 ]
-            },
-            "10": {
-                "text": "\nProvide a list of instructions for preparing ch",
-                "token_ids": [ 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663 ],
-                "tokens": [ "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch" ],
-                "logprobs": [ -0.03829614818096161, -0.8993099927902222, -0.269023060798645, -0.29307451844215393, -0.0057315765880048275, -0.15898968279361725, -0.12841321527957916, -0.28819355368614197, -0.0024942022282630205, -0.2027340531349182 ]
-            },
-            "1": {
-                "text": "\n",
-                "token_ids": [ 203 ],
-                "tokens": [ "\n" ],
-                "logprobs": [ -0.03829614818096161 ]
-            },
-            "6": {
-                "text": "\nProvide a list of instructions",
-                "token_ids": [ 203, 15277, 312, 1149, 432, 9400 ],
-                "tokens": [ "\n", "Provide", " a", " list", " of", " instructions" ],
-                "logprobs": [ -0.03829603269696236, -0.8993070721626282, -0.26902350783348083, -0.2930747866630554, -0.0057315765880048275, -0.15899120271205902 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide me a list of things that I can do with my new found wealth.\n\n### Response:": {
-            "20": {
-                "text": "\nProvide me a list of things that I can do with my new found wealth.\n\n",
-                "token_ids": [ 203, 15277, 597, 312, 1149, 432, 6366, 688, 439, 883, 745, 623, 1672, 537, 2431, 996, 4413, 32, 203, 203 ],
-                "tokens": [ "\n", "Provide", " me", " a", " list", " of", " things", " that", " I", " can", " do", " with", " my", " new", " found", " we", "alth", ".", "\n", "\n" ],
-                "logprobs": [ -0.03284458816051483, -1.2381832599639893, -1.107851505279541, -0.32815009355545044, -0.29831045866012573, -0.015158339403569698, -0.07985367625951767, -0.10023558884859085, -0.1060047298669815, -0.07053174078464508, -0.037321269512176514, -0.019136684015393257, -0.005681556649506092, -0.0471559576690197, -0.0869126170873642, -0.005113617982715368, -0.00025662468397058547, -0.049012504518032074, -0.20452581346035004, -0.023316141217947006 ]
-            },
-            "10": {
-                "text": "\nProvide me a list of things that I can",
-                "token_ids": [ 203, 15277, 597, 312, 1149, 432, 6366, 688, 439, 883 ],
-                "tokens": [ "\n", "Provide", " me", " a", " list", " of", " things", " that", " I", " can" ],
-                "logprobs": [ -0.03284458816051483, -1.2381832599639893, -1.107851505279541, -0.32815009355545044, -0.29831045866012573, -0.015158339403569698, -0.07985367625951767, -0.10023558884859085, -0.1060047298669815, -0.07053174078464508 ]
-            },
-            "6": {
-                "text": "\nProvide me a list of",
-                "token_ids": [ 203, 15277, 597, 312, 1149, 432 ],
-                "tokens": [ "\n", "Provide", " me", " a", " list", " of" ],
-                "logprobs": [ -0.03284458816051483, -1.2381811141967773, -1.1078497171401978, -0.3281524181365967, -0.2983091175556183, -0.015158339403569698 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nhow do I add multiple new columns in m for power query or                 power bi?\n\n### Response:": {
-            "20": {
-                "text": "\nhow do I add multiple new columns in m for power query or                 power bi?\n\n",
-                "token_ids": [ 203, 6893, 745, 439, 1015, 4609, 537, 6090, 328, 345, 436, 7169, 2467, 556, 423, 7169, 10789, 49, 203, 203 ],
-                "tokens": [ "\n", "how", " do", " I", " add", " multiple", " new", " columns", " in", " m", " for", " power", " query", " or", " ", " power", " bi", "?", "\n", "\n" ],
-                "logprobs": [ -0.03021050989627838, -1.1658661365509033, -0.11387945711612701, -0.058720510452985764, -0.28079384565353394, -0.11458290368318558, -0.3235940635204315, -0.01923912763595581, -0.016317032277584076, -0.0621035173535347, -0.0764622613787651, -0.07680057734251022, -0.6789519190788269, -0.036087214946746826, -0.697851300239563, -0.013305091299116611, -0.05908589065074921, -0.02506561577320099, -0.1240944117307663, -0.07023497670888901 ]
-            },
-            "10": {
-                "text": "\nhow do I add multiple new columns in m",
-                "token_ids": [ 203, 6893, 745, 439, 1015, 4609, 537, 6090, 328, 345 ],
-                "tokens": [ "\n", "how", " do", " I", " add", " multiple", " new", " columns", " in", " m" ],
-                "logprobs": [ -0.03021050989627838, -1.1658661365509033, -0.11387945711612701, -0.058720510452985764, -0.28079384565353394, -0.11458290368318558, -0.3235940635204315, -0.01923912763595581, -0.016317032277584076, -0.0621035173535347 ]
-            },
-            "6": {
-                "text": "\nhow do I add multiple",
-                "token_ids": [ 203, 6893, 745, 439, 1015, 4609 ],
-                "tokens": [ "\n", "how", " do", " I", " add", " multiple" ],
-                "logprobs": [ -0.03021039441227913, -1.1658703088760376, -0.11387977749109268, -0.058720286935567856, -0.28079313039779663, -0.11458311229944229 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nConvert char to string in Java.\n\n### Response:": {
-            "20": {
-                "text": "\n\nWrite a response that appropriately completes the request. Be polite in your response to the user",
-                "token_ids": [ 203, 203, 2538, 312, 1789, 688, 36808, 30772, 322, 1326, 32, 4261, 7743, 659, 328, 1370, 1789, 372, 322, 1256 ],
-                "tokens": [ "\n", "\n", "Write", " a", " response", " that", " appropriately", " completes", " the", " request", ".", " Be", " pol", "ite", " in", " your", " response", " to", " the", " user" ],
-                "logprobs": [ -0.029220648109912872, -1.4119871854782104, -2.613886594772339, -0.3121708333492279, -2.008859395980835, -0.6761901378631592, -0.6125637888908386, -0.07957419753074646, -0.016299674287438393, -0.07285373657941818, -0.032475605607032776, -0.4773809611797333, -0.00326825981028378, -0.0002727136597968638, -0.008147811517119408, -0.017394136637449265, -0.017149491235613823, -0.03445310890674591, -0.005661049857735634, -0.039977941662073135 ]
-            },
-            "5": {
-                "text": "\n\nWrite a response",
-                "token_ids": [ 203, 203, 2538, 312, 1789 ],
-                "tokens": [ "\n", "\n", "Write", " a", " response" ],
-                "logprobs": [ -0.029220648109912872, -1.4119871854782104, -2.613886594772339, -0.3121708333492279, -2.008859395980835 ]
-            },
-            "1": {
-                "text": "\n",
-                "token_ids": [ 203 ],
-                "tokens": [ "\n" ],
-                "logprobs": [ -0.029220648109912872 ]
-            },
-            "10": {
-                "text": "\n\nWrite a response that appropriately completes the request",
-                "token_ids": [ 203, 203, 2538, 312, 1789, 688, 36808, 30772, 322, 1326 ],
-                "tokens": [ "\n", "\n", "Write", " a", " response", " that", " appropriately", " completes", " the", " request" ],
-                "logprobs": [ -0.029220648109912872, -1.4119871854782104, -2.613886594772339, -0.3121708333492279, -2.008859395980835, -0.6761901378631592, -0.6125637888908386, -0.07957419753074646, -0.016299674287438393, -0.07285373657941818 ]
-            },
-            "6": {
-                "text": "\n\nWrite a response that",
-                "token_ids": [ 203, 203, 2538, 312, 1789, 688 ],
-                "tokens": [ "\n", "\n", "Write", " a", " response", " that" ],
-                "logprobs": [ -0.029220648109912872, -1.4119877815246582, -2.6138856410980225, -0.3121702969074249, -2.008859872817993, -0.6761930584907532 ]
-            }
-        },
-        "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203": {
-            "30": {
-                "text": "The following is an example of a task that can be done with a single request.\n\nThe following is an example of a task that can be",
-                "token_ids": [ 1318, 2412, 438, 600, 2280, 432, 312, 2899, 688, 883, 526, 3390, 623, 312, 3982, 1326, 32, 203, 203, 1318, 2412, 438, 600, 2280, 432, 312, 2899, 688, 883, 526 ],
-                "tokens": [ "The", " following", " is", " an", " example", " of", " a", " task", " that", " can", " be", " done", " with", " a", " single", " request", ".", "\n", "\n", "The", " following", " is", " an", " example", " of", " a", " task", " that", " can", " be" ],
-                "logprobs": [ -2.546842575073242, -2.468151092529297, -1.642992377281189, -0.8562104105949402, -0.6219075322151184, -0.26631927490234375, -0.6193063259124756, -2.3995795249938965, -1.0385791063308716, -1.9017250537872314, -0.1650543212890625, -1.8853514194488525, -1.339092493057251, -1.175339937210083, -2.424269676208496, -2.3726375102996826, -0.820810079574585, -1.1801930665969849, -0.037204235792160034, -1.76130211353302, -1.0623141527175903, -0.5799030661582947, -0.42776262760162354, -0.12721644341945648, -0.12382703274488449, -0.17553317546844482, -0.5803451538085938, -0.05632644519209862, -0.1108473390340805, -0.018181920051574707 ]
-            },
-            "55": {
-                "text": "The following is an example of a task that can be done with a single request.\n\nThe following is an example of a task that can be done with a single request.\n\nThe following is an example of a task that can be done with a single request.",
-                "token_ids": [ 1318, 2412, 438, 600, 2280, 432, 312, 2899, 688, 883, 526, 3390, 623, 312, 3982, 1326, 32, 203, 203, 1318, 2412, 438, 600, 2280, 432, 312, 2899, 688, 883, 526, 3390, 623, 312, 3982, 1326, 32, 203, 203, 1318, 2412, 438, 600, 2280, 432, 312, 2899, 688, 883, 526, 3390, 623, 312, 3982, 1326, 32 ],
-                "tokens": [ "The", " following", " is", " an", " example", " of", " a", " task", " that", " can", " be", " done", " with", " a", " single", " request", ".", "\n", "\n", "The", " following", " is", " an", " example", " of", " a", " task", " that", " can", " be", " done", " with", " a", " single", " request", ".", "\n", "\n", "The", " following", " is", " an", " example", " of", " a", " task", " that", " can", " be", " done", " with", " a", " single", " request", "." ],
-                "logprobs": [ -2.546842098236084, -2.468151807785034, -1.6429933309555054, -0.856209933757782, -0.6219078302383423, -0.26631900668144226, -0.6193069815635681, -2.39958119392395, -1.0385786294937134, -1.9017254114151, -0.16505472362041473, -1.8853517770767212, -1.3390932083129883, -1.1753389835357666, -2.4242682456970215, -2.3726353645324707, -0.8208092451095581, -1.1801929473876953, -0.037204235792160034, -1.7613036632537842, -1.0623137950897217, -0.5799035429954529, -0.42776215076446533, -0.12721623480319977, -0.12382713705301285, -0.17553338408470154, -0.5803452134132385, -0.056326329708099365, -0.11084754765033722, -0.018181920051574707, -0.04614562913775444, -0.0928993821144104, -0.6252247095108032, -0.03943007439374924, -0.5119109153747559, -0.13451874256134033, -0.36121895909309387, -0.007122127804905176, -1.0829120874404907, -0.2997977137565613, -0.21955722570419312, -0.2015833854675293, -0.03804818540811539, -0.022764425724744797, -0.1090807244181633, -0.2392035722732544, -0.013668631203472614, -0.049756381660699844, -0.009701485745608807, -0.020750457420945168, -0.04941253736615181, -0.34746482968330383, -0.03515138477087021, -0.20074909925460815, -0.06580076366662979 ]
-            },
-            "4": {
-                "text": "The following is an",
-                "token_ids": [ 1318, 2412, 438, 600 ],
-                "tokens": [ "The", " following", " is", " an" ],
-                "logprobs": [ -2.546842098236084, -2.468151807785034, -1.6429933309555054, -0.856209933757782 ]
-            }
-        },
-        "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743": {
-            "10": {
-                "text": "ite and professional.\n\nThe following is an",
-                "token_ids": [ 659, 461, 39073, 32, 203, 203, 1318, 2412, 438, 600 ],
-                "tokens": [ "ite", " and", " professional", ".", "\n", "\n", "The", " following", " is", " an" ],
-                "logprobs": [ -0.0027531597297638655, -0.8262372016906738, -1.6557724475860596, -0.9526425004005432, -1.3980242013931274, -0.17829635739326477, -2.773099660873413, -2.538527250289917, -1.3751128911972046, -0.8114928603172302 ]
-            },
-            "3": {
-                "text": "ite and professional",
-                "token_ids": [ 659, 461, 39073 ],
-                "tokens": [ "ite", " and", " professional" ],
-                "logprobs": [ -0.0027532787062227726, -0.8262380361557007, -1.6557728052139282 ]
-            }
-        },
-        "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808": {
-            "52": {
-                "text": " describes the task.\n\nThe task is a 3-minute video that explains the process of writing a 3-minute response.\n\nThe video is a 3-minute video that explains the process of writing a 3-minute response.",
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-                "logprobs": [ -1.0128189325332642, -1.9446691274642944, -0.4778711199760437 ]
-            }
-        },
-        "__tokens__27400_438_600_12404_688": {
-            "2": {
-                "text": " will help",
-                "token_ids": [ 1098, 3049 ],
-                "tokens": [ " will", " help" ],
-                "logprobs": [ -2.4841904640197754, -1.7338652610778809 ]
-            }
-        },
-        "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623_540_565_2738_994_372_1983_623_561_1259_688_322_36467_438_36986_461_1930_3654": {
-            "2": {
-                "text": " that you",
-                "token_ids": [ 688, 844 ],
-                "tokens": [ " that", " you" ],
-                "logprobs": [ -0.9784514307975769, -1.4096184968948364 ]
-            }
-        },
-        "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623_540_565_2738_994_372_1983_623_561_1259_688_322_36467_438_36986_461_1930_3654_372": {
-            "2": {
-                "text": " include the",
-                "token_ids": [ 2305, 322 ],
-                "tokens": [ " include", " the" ],
-                "logprobs": [ -2.6323490142822266, -1.6850149631500244 ]
+        "6e9c6465a9d7e5e9fa35004a29f0c90befa7d23f": {
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup.\n\n### Response:": {
+                "20": {
+                    "text": "\n\n1.\n\nThe user will receive a list of instructions for preparing chicken soup",
+                    "token_ids": [ 203, 203, 35, 32, 203, 203, 1318, 1256, 1098, 7768, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628 ],
+                    "tokens": [ "\n", "\n", "1", ".", "\n", "\n", "The", " user", " will", " receive", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup" ],
+                    "logprobs": [ -0.03913814201951027, -1.462918996810913, -2.2675652503967285, -0.108219675719738, -2.4334919452667236, -0.7974978685379028, -2.302020788192749, -2.239565372467041, -1.972684383392334, -1.080353021621704, -0.6740201711654663, -2.055830717086792, -0.0200518649071455, -0.5573933124542236, -1.0624492168426514, -0.0893486887216568, -0.0003405229654163122, -0.0869663879275322, -0.00038914260221645236, -0.010050169192254543 ]
+                },
+                "5": {
+                    "text": "\n\n1.\n",
+                    "token_ids": [ 203, 203, 35, 32, 203 ],
+                    "tokens": [ "\n", "\n", "1", ".", "\n" ],
+                    "logprobs": [ -0.03913814201951027, -1.462918996810913, -2.2675652503967285, -0.108219675719738, -2.4334919452667236 ]
+                },
+                "6": {
+                    "text": "\n\n1.\n\n",
+                    "token_ids": [ 203, 203, 35, 32, 203, 203 ],
+                    "tokens": [ "\n", "\n", "1", ".", "\n", "\n" ],
+                    "logprobs": [ -0.03913814201951027, -1.462918996810913, -2.2675652503967285, -0.108219675719738, -2.4334919452667236, -0.7974978685379028 ]
+                },
+                "1": {
+                    "text": "\n",
+                    "token_ids": [ 203 ],
+                    "tokens": [ "\n" ],
+                    "logprobs": [ -0.03913814201951027 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide me a list of things that I can do with my new found wealth.\n\n### Response:": {
+                "20": {
+                    "text": "\n\nI will be happy to help you with your task.\n\n### Instructions:\n\n",
+                    "token_ids": [ 203, 203, 59, 1098, 526, 15818, 372, 3049, 844, 623, 1370, 2899, 32, 203, 203, 1482, 34684, 44, 203, 203 ],
+                    "tokens": [ "\n", "\n", "I", " will", " be", " happy", " to", " help", " you", " with", " your", " task", ".", "\n", "\n", "###", " Instructions", ":", "\n", "\n" ],
+                    "logprobs": [ -0.03954844921827316, -1.3203511238098145, -2.2519102096557617, -1.8162301778793335, -1.7537648677825928, -2.4581241607666016, -0.06160799413919449, -1.3357799053192139, -0.3970973789691925, -0.8492777347564697, -0.9651142954826355, -1.6141713857650757, -0.2954912781715393, -0.5494982004165649, -0.0777588039636612, -0.7150753140449524, -2.652331829071045, -0.1676260083913803, -0.024376431480050087, -0.4526679217815399 ]
+                },
+                "6": {
+                    "text": "\n\nI will be happy",
+                    "token_ids": [ 203, 203, 59, 1098, 526, 15818 ],
+                    "tokens": [ "\n", "\n", "I", " will", " be", " happy" ],
+                    "logprobs": [ -0.03954844921827316, -1.3203511238098145, -2.2519102096557617, -1.8162301778793335, -1.7537648677825928, -2.4581241607666016 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nhow do I add multiple new columns in m for power query or                 power bi?\n\n### Response:": {
+                "20": {
+                    "text": "\n\nI am trying to add a new column in a table. I have a table called \"",
+                    "token_ids": [ 203, 203, 59, 3860, 7667, 372, 1015, 312, 537, 2623, 328, 312, 1858, 32, 439, 1159, 312, 1858, 3823, 313 ],
+                    "tokens": [ "\n", "\n", "I", " am", " trying", " to", " add", " a", " new", " column", " in", " a", " table", ".", " I", " have", " a", " table", " called", " \"" ],
+                    "logprobs": [ -0.057651810348033905, -1.2759267091751099, -3.0963268280029297, -2.0452775955200195, -1.9688712358474731, -0.05093619227409363, -0.7732970118522644, -1.5025215148925781, -0.40873071551322937, -0.16327306628227234, -1.2194674015045166, -1.7171523571014404, -0.8744009733200073, -2.03483510017395, -1.496689796447754, -1.4733734130859375, -1.5641465187072754, -1.4802309274673462, -1.1941152811050415, -1.3049496412277222 ]
+                },
+                "10": {
+                    "text": "\n\nI am trying to add a new column",
+                    "token_ids": [ 203, 203, 59, 3860, 7667, 372, 1015, 312, 537, 2623 ],
+                    "tokens": [ "\n", "\n", "I", " am", " trying", " to", " add", " a", " new", " column" ],
+                    "logprobs": [ -0.057651810348033905, -1.2759267091751099, -3.0963268280029297, -2.0452775955200195, -1.9688712358474731, -0.05093619227409363, -0.7732970118522644, -1.5025215148925781, -0.40873071551322937, -0.16327306628227234 ]
+                },
+                "6": {
+                    "text": "\n\nI am trying to",
+                    "token_ids": [ 203, 203, 59, 3860, 7667, 372 ],
+                    "tokens": [ "\n", "\n", "I", " am", " trying", " to" ],
+                    "logprobs": [ -0.057651810348033905, -1.2759267091751099, -3.0963268280029297, -2.0452775955200195, -1.9688712358474731, -0.05093619227409363 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nConvert char to string in Java.\n\n### Response:": {
+                "20": {
+                    "text": "\n\n```java\nString s = \"Hello, world!\";\nString str = s.toString",
+                    "token_ids": [ 203, 203, 914, 1859, 203, 652, 309, 280, 313, 8279, 30, 5788, 23972, 203, 652, 596, 280, 309, 32, 3108 ],
+                    "tokens": [ "\n", "\n", "```", "java", "\n", "String", " s", " =", " \"", "Hello", ",", " world", "!\";", "\n", "String", " str", " =", " s", ".", "toString" ],
+                    "logprobs": [ -0.045518096536397934, -1.1725132465362549, -2.297534942626953, -1.8406871557235718, -0.08397945761680603, -1.1759214401245117, -1.8778071403503418, -0.18092676997184753, -0.38083940744400024, -1.342236876487732, -1.3371635675430298, -0.7441288232803345, -0.7093914747238159, -0.05011586844921112, -1.4100021123886108, -1.6390646696090698, -0.05425424873828888, -0.45294442772865295, -0.16424795985221863, -1.4751557111740112 ]
+                },
+                "5": {
+                    "text": "\n\n```java\n",
+                    "token_ids": [ 203, 203, 914, 1859, 203 ],
+                    "tokens": [ "\n", "\n", "```", "java", "\n" ],
+                    "logprobs": [ -0.045518096536397934, -1.1725132465362549, -2.297534942626953, -1.8406871557235718, -0.08397945761680603 ]
+                },
+                "1": {
+                    "text": "\n",
+                    "token_ids": [ 203 ],
+                    "tokens": [ "\n" ],
+                    "logprobs": [ -0.045518096536397934 ]
+                },
+                "6": {
+                    "text": "\n\n```java\nString",
+                    "token_ids": [ 203, 203, 914, 1859, 203, 652 ],
+                    "tokens": [ "\n", "\n", "```", "java", "\n", "String" ],
+                    "logprobs": [ -0.045518096536397934, -1.1725132465362549, -2.297534942626953, -1.8406871557235718, -0.08397945761680603, -1.1759214401245117 ]
+                }
+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623_540_565": {
+                "10": {
+                    "text": " and vegetables.\n\n### Instruction:",
+                    "token_ids": [ 461, 5292, 371, 6975, 32, 203, 203, 1482, 21081, 44 ],
+                    "tokens": [ " and", " ve", "get", "ables", ".", "\n", "\n", "###", " Instruction", ":" ],
+                    "logprobs": [ -1.3438630104064941, -1.0608100891113281, -0.00044371772673912346, -0.05484585836529732, -0.31068387627601624, -0.82936030626297, -0.3198590576648712, -0.5727909803390503, -2.038623094558716, -0.030490949749946594 ]
+                },
+                "4": {
+                    "text": " and vegetables",
+                    "token_ids": [ 461, 5292, 371, 6975 ],
+                    "tokens": [ " and", " ve", "get", "ables" ],
+                    "logprobs": [ -1.3438630104064941, -1.0608100891113281, -0.00044371772673912346, -0.05484585836529732 ]
+                },
+                "18": {
+                    "text": " and vegetables.\n\n### Instruction:\nProvide a list of instructions for pre",
+                    "token_ids": [ 461, 5292, 371, 6975, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406 ],
+                    "tokens": [ " and", " ve", "get", "ables", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre" ],
+                    "logprobs": [ -1.3438630104064941, -1.0608100891113281, -0.00044371772673912346, -0.05484585836529732, -0.31068387627601624, -0.82936030626297, -0.3198590576648712, -0.5727909803390503, -2.038623094558716, -0.030490949749946594, -0.01894744299352169, -1.9958713054656982, -0.4056839644908905, -0.3856453597545624, -0.004885996226221323, -0.6480128765106201, -0.09622390568256378, -0.6480308771133423 ]
+                },
+                "65": {
+                    "text": " and vegetables.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice and vegetables.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice and vegetables.\n\n### Instruction:\nProvide a",
+                    "token_ids": [ 461, 5292, 371, 6975, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 461, 5292, 371, 6975, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 461, 5292, 371, 6975, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312 ],
+                    "tokens": [ " and", " ve", "get", "ables", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", " and", " ve", "get", "ables", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", " and", " ve", "get", "ables", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a" ],
+                    "logprobs": [ -1.3438630104064941, -1.0608100891113281, -0.00044371772673912346, -0.05484585836529732, -0.31068387627601624, -0.82936030626297, -0.3198590576648712, -0.5727909803390503, -2.038623094558716, -0.030490949749946594, -0.01894744299352169, -1.9958713054656982, -0.4056839644908905, -0.3856453597545624, -0.004885996226221323, -0.6480128765106201, -0.09622390568256378, -0.6480308175086975, -0.0009054613183252513, -0.3120970129966736, -0.0014035383937880397, -0.033141590654850006, -0.06854391098022461, -0.0036948525812476873, -0.11483650654554367, -0.00044216870446689427, -0.0077491383999586105, -0.007830518297851086, -5.9126061387360096e-05, -0.0010649253381416202, -0.02066848799586296, -0.3883886933326721, -0.11688299477100372, -0.23739460110664368, -0.7001675367355347, -0.014315995387732983, -0.010078610852360725, -0.7550809383392334, -0.05727194622159004, -0.06725609302520752, -0.0007068996201269329, -0.07397761940956116, -0.013498933054506779, -0.06803742051124573, -0.0004992430913262069, -0.06138782948255539, -0.00032085992279462516, -0.004015360493212938, -0.016522133722901344, -0.001544950413517654, -0.028345460072159767, -0.00019298121333122253, -0.003546854481101036, -0.004785275552421808, -6.639736966462806e-05, -0.0005653690313920379, -0.014165212400257587, -0.1466725468635559, -0.03276694938540459, -0.1971900463104248, -0.36776840686798096, -0.006899580359458923, -0.007371606770902872, -0.31242474913597107, -0.021978147327899933 ]
+                },
+                "67": {
+                    "text": " and vegetables.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice and vegetables.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice and vegetables.\n\n### Instruction:\nProvide a list of",
+                    "token_ids": [ 461, 5292, 371, 6975, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 461, 5292, 371, 6975, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 461, 5292, 371, 6975, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432 ],
+                    "tokens": [ " and", " ve", "get", "ables", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", " and", " ve", "get", "ables", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", " and", " ve", "get", "ables", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of" ],
+                    "logprobs": [ -1.3438630104064941, -1.0608100891113281, -0.00044371772673912346, -0.05484585836529732, -0.31068387627601624, -0.82936030626297, -0.3198590576648712, -0.5727909803390503, -2.038623094558716, -0.030490949749946594, -0.01894744299352169, -1.9958713054656982, -0.4056839644908905, -0.3856453597545624, -0.004885996226221323, -0.6480128765106201, -0.09622390568256378, -0.6480308175086975, -0.0009054613183252513, -0.3120970129966736, -0.0014035383937880397, -0.033141590654850006, -0.06854391098022461, -0.0036948525812476873, -0.11483650654554367, -0.00044216870446689427, -0.0077491383999586105, -0.007830518297851086, -5.9126061387360096e-05, -0.0010649253381416202, -0.02066848799586296, -0.3883886933326721, -0.11688299477100372, -0.23739460110664368, -0.7001675367355347, -0.014315995387732983, -0.010078610852360725, -0.7550809383392334, -0.05727194622159004, -0.06725609302520752, -0.0007068996201269329, -0.07397761940956116, -0.013498933054506779, -0.06803742051124573, -0.0004992430913262069, -0.06138782948255539, -0.00032085992279462516, -0.004015360493212938, -0.016522133722901344, -0.001544950413517654, -0.028345460072159767, -0.00019298121333122253, -0.003546854481101036, -0.004785275552421808, -6.639736966462806e-05, -0.0005653690313920379, -0.014165212400257587, -0.1466725468635559, -0.03276694938540459, -0.1971900463104248, -0.36776840686798096, -0.006899580359458923, -0.007371606770902872, -0.31242474913597107, -0.021978147327899933, -0.041798461228609085, -0.00031680811662226915 ]
+                },
+                "60": {
+                    "text": " and vegetables.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice and vegetables.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice and vegetables.\n\n###",
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+                    "tokens": [ "\n", "\n", "Task" ],
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+                }
             }
         }
     },
     "JackFram/llama-160m": {
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup.\n\n### Response:": {
-            "20": {
-                "text": "Provide a list of instructions for preparing chicken soup.\nProvide a list of",
-                "token_ids": [ 9133, 680, 263, 1051, 310, 11994, 363, 10223, 292, 521, 21475, 22300, 29889, 13, 1184, 29894, 680, 263, 1051, 310 ],
-                "tokens": [ "Prov", "ide", "a", "list", "of", "instructions", "for", "prepar", "ing", "ch", "icken", "soup", ".", "\n", "Pro", "v", "ide", "a", "list", "of" ],
-                "logprobs": [ -1.967644214630127, -0.04043347015976906, -0.508905827999115, -0.20296810567378998, -0.0020171310752630234, -0.08453292399644852, -0.03589482977986336, -0.07238712906837463, -0.0007784912013448775, -0.2117457538843155, -0.006608654744923115, -0.019976381212472916, -0.032455988228321075, -0.13490653038024902, -1.6157530546188354, -0.027694014832377434, -0.01953446865081787, -0.3970067501068115, -0.08640026301145554, -0.001720973290503025 ]
-            },
-            "5": {
-                "text": "Provide a list of",
-                "token_ids": [ 9133, 680, 263, 1051, 310 ],
-                "tokens": [ "Prov", "ide", "a", "list", "of" ],
-                "logprobs": [ -1.967644214630127, -0.04043347015976906, -0.508905827999115, -0.20296810567378998, -0.0020171310752630234 ]
-            },
-            "10": {
-                "text": "Provide a list of instructions for preparing ch",
-                "token_ids": [ 9133, 680, 263, 1051, 310, 11994, 363, 10223, 292, 521 ],
-                "tokens": [ "Prov", "ide", "a", "list", "of", "instructions", "for", "prepar", "ing", "ch" ],
-                "logprobs": [ -1.967644214630127, -0.04043347015976906, -0.508905827999115, -0.20296810567378998, -0.0020171310752630234, -0.08453292399644852, -0.03589482977986336, -0.07238712906837463, -0.0007784912013448775, -0.21174585819244385 ]
-            },
-            "1": {
-                "text": "Prov",
-                "token_ids": [ 9133 ],
-                "tokens": [ "Prov" ],
-                "logprobs": [ -1.967644214630127 ]
-            },
-            "6": {
-                "text": "Provide a list of instructions",
-                "token_ids": [ 9133, 680, 263, 1051, 310, 11994 ],
-                "tokens": [ "Prov", "ide", "a", "list", "of", "instructions" ],
-                "logprobs": [ -1.9676533937454224, -0.040433239191770554, -0.5089076161384583, -0.20296713709831238, -0.0020171310752630234, -0.08453259617090225 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide me a list of things that I can do with my new found wealth.\n\n### Response:": {
-            "20": {
-                "text": "I want to buy a house.\n### Response: I want to buy a house.\n",
-                "token_ids": [ 306, 864, 304, 15649, 263, 3699, 29889, 13, 2277, 29937, 13291, 29901, 306, 864, 304, 15649, 263, 3699, 29889, 13 ],
-                "tokens": [ "I", "want", "to", "buy", "a", "house", ".", "\n", "##", "#", "Response", ":", "I", "want", "to", "buy", "a", "house", ".", "\n" ],
-                "logprobs": [ -1.347567081451416, -1.736817717552185, -0.22189107537269592, -2.632402181625366, -0.8735470771789551, -1.2113419771194458, -0.5731930732727051, -0.11853943765163422, -1.3035320043563843, -0.0031280419789254665, -0.3427315950393677, -0.013111918233335018, -0.31673598289489746, -0.18097272515296936, -0.04263797029852867, -0.8929623961448669, -0.11564759910106659, -0.6950972080230713, -0.0544249601662159, -0.07290516048669815 ]
-            },
-            "10": {
-                "text": "I want to buy a house.\n###",
-                "token_ids": [ 306, 864, 304, 15649, 263, 3699, 29889, 13, 2277, 29937 ],
-                "tokens": [ "I", "want", "to", "buy", "a", "house", ".", "\n", "##", "#" ],
-                "logprobs": [ -1.347567081451416, -1.736817717552185, -0.22189107537269592, -2.632402181625366, -0.8735470771789551, -1.2113419771194458, -0.5731930732727051, -0.11853943765163422, -1.3035320043563843, -0.0031280419789254665 ]
-            },
-            "6": {
-                "text": "I want to buy a house",
-                "token_ids": [ 306, 864, 304, 15649, 263, 3699 ],
-                "tokens": [ "I", "want", "to", "buy", "a", "house" ],
-                "logprobs": [ -1.3475680351257324, -1.7368229627609253, -0.22189192473888397, -2.6323986053466797, -0.8735487461090088, -1.2113405466079712 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nhow do I add multiple new columns in m for power query or                 power bi?\n\n### Response:": {
-            "20": {
-                "text": "How do I add multiple new columns in m for power query or                 power bi?\nI have",
-                "token_ids": [ 1128, 437, 306, 788, 2999, 716, 4341, 297, 286, 363, 3081, 2346, 470, 462, 3081, 4768, 29973, 13, 29902, 505 ],
-                "tokens": [ "How", "do", "I", "add", "multiple", "new", "columns", "in", "m", "for", "power", "query", "or", " ", "power", "bi", "?", "\n", "I", "have" ],
-                "logprobs": [ -2.0655946731567383, -0.5818344354629517, -0.1046733632683754, -0.5874699354171753, -0.1780477613210678, -0.08541657775640488, -0.04755059629678726, -0.013712609186768532, -0.17736363410949707, -0.01853116974234581, -0.007231844589114189, -0.009783296845853329, -0.07222001254558563, -0.4528859257698059, -0.012942596338689327, -0.08836681395769119, -0.019914798438549042, -0.14132876694202423, -1.8851993083953857, -1.6479270458221436 ]
-            },
-            "10": {
-                "text": "How do I add multiple new columns in m for",
-                "token_ids": [ 1128, 437, 306, 788, 2999, 716, 4341, 297, 286, 363 ],
-                "tokens": [ "How", "do", "I", "add", "multiple", "new", "columns", "in", "m", "for" ],
-                "logprobs": [ -2.0655946731567383, -0.5818343758583069, -0.1046733632683754, -0.5874699354171753, -0.1780477613210678, -0.08541657775640488, -0.04755059629678726, -0.013712609186768532, -0.17736363410949707, -0.01853116974234581 ]
-            },
-            "6": {
-                "text": "How do I add multiple new",
-                "token_ids": [ 1128, 437, 306, 788, 2999, 716 ],
-                "tokens": [ "How", "do", "I", "add", "multiple", "new" ],
-                "logprobs": [ -2.065593957901001, -0.5818334817886353, -0.10467303544282913, -0.5874689817428589, -0.17804735898971558, -0.08541624993085861 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nConvert char to string in Java.\n\n### Response:": {
-            "20": {
-                "text": "\"Hello, welcome to the Java community!\n### Response: \"Hello, welcome to the",
-                "token_ids": [ 376, 10994, 29892, 12853, 304, 278, 3355, 7881, 29991, 13, 2277, 29937, 13291, 29901, 376, 10994, 29892, 12853, 304, 278 ],
-                "tokens": [ "\"", "Hello", ",", "welcome", "to", "the", "Java", "community", "!", "\n", "##", "#", "Response", ":", "\"", "Hello", ",", "welcome", "to", "the" ],
-                "logprobs": [ -1.56131911277771, -1.9557151794433594, -0.5875644087791443, -2.0813868045806885, -0.3190169334411621, -1.161803960800171, -1.5128810405731201, -1.3871722221374512, -1.197195291519165, -1.1658143997192383, -1.2232141494750977, -0.036547161638736725, -0.343057245016098, -0.027858886867761612, -0.037200216203927994, -0.5646355152130127, -0.024805627763271332, -0.12805511057376862, -0.023057445883750916, -0.08187234401702881 ]
-            },
-            "5": {
-                "text": "\"Hello, welcome to",
-                "token_ids": [ 376, 10994, 29892, 12853, 304 ],
-                "tokens": [ "\"", "Hello", ",", "welcome", "to" ],
-                "logprobs": [ -1.56131911277771, -1.9557151794433594, -0.5875644087791443, -2.0813868045806885, -0.3190169334411621 ]
-            },
-            "1": {
-                "text": "\"",
-                "token_ids": [ 376 ],
-                "tokens": [ "\"" ],
-                "logprobs": [ -1.56131911277771 ]
-            },
-            "10": {
-                "text": "\"Hello, welcome to the Java community!\n",
-                "token_ids": [ 376, 10994, 29892, 12853, 304, 278, 3355, 7881, 29991, 13 ],
-                "tokens": [ "\"", "Hello", ",", "welcome", "to", "the", "Java", "community", "!", "\n" ],
-                "logprobs": [ -1.56131911277771, -1.9557151794433594, -0.5875644087791443, -2.0813868045806885, -0.3190169334411621, -1.161803960800171, -1.5128810405731201, -1.3871722221374512, -1.197195291519165, -1.1658143997192383 ]
-            },
-            "6": {
-                "text": "\"Hello, welcome to the",
-                "token_ids": [ 376, 10994, 29892, 12853, 304, 278 ],
-                "tokens": [ "\"", "Hello", ",", "welcome", "to", "the" ],
-                "logprobs": [ -1.5613254308700562, -1.955716848373413, -0.5875644683837891, -2.0813889503479004, -0.3190162479877472, -1.1618010997772217 ]
+        "no-revision": {
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup.\n\n### Response:": {
+                "20": {
+                    "text": "Provide a list of instructions for preparing chicken soup.\nProvide a list of",
+                    "token_ids": [ 9133, 680, 263, 1051, 310, 11994, 363, 10223, 292, 521, 21475, 22300, 29889, 13, 1184, 29894, 680, 263, 1051, 310 ],
+                    "tokens": [ "Prov", "ide", "a", "list", "of", "instructions", "for", "prepar", "ing", "ch", "icken", "soup", ".", "\n", "Pro", "v", "ide", "a", "list", "of" ],
+                    "logprobs": [ -1.9676533937454224, -0.040433239191770554, -0.5089076161384583, -0.20296713709831238, -0.0020171310752630234, -0.08453259617090225, -0.03589482977986336, -0.07238690555095673, -0.0007784912013448775, -0.2117452770471573, -0.006608536001294851, -0.019976265728473663, -0.032455988228321075, -0.13490621745586395, -1.6157575845718384, -0.027694132179021835, -0.019534585997462273, -0.3970077931880951, -0.08640026301145554, -0.001720973290503025 ]
+                },
+                "5": {
+                    "text": "Provide a list of",
+                    "token_ids": [ 9133, 680, 263, 1051, 310 ],
+                    "tokens": [ "Prov", "ide", "a", "list", "of" ],
+                    "logprobs": [ -1.9676533937454224, -0.040433239191770554, -0.5089076161384583, -0.20296713709831238, -0.0020171310752630234 ]
+                },
+                "6": {
+                    "text": "Provide a list of instructions",
+                    "token_ids": [ 9133, 680, 263, 1051, 310, 11994 ],
+                    "tokens": [ "Prov", "ide", "a", "list", "of", "instructions" ],
+                    "logprobs": [ -1.9676533937454224, -0.040433239191770554, -0.5089076161384583, -0.20296713709831238, -0.0020171310752630234, -0.08453259617090225 ]
+                },
+                "1": {
+                    "text": "Prov",
+                    "token_ids": [ 9133 ],
+                    "tokens": [ "Prov" ],
+                    "logprobs": [ -1.9676533937454224 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide me a list of things that I can do with my new found wealth.\n\n### Response:": {
+                "20": {
+                    "text": "I want to buy a house.\n### Response: I want to buy a house.\n",
+                    "token_ids": [ 306, 864, 304, 15649, 263, 3699, 29889, 13, 2277, 29937, 13291, 29901, 306, 864, 304, 15649, 263, 3699, 29889, 13 ],
+                    "tokens": [ "I", "want", "to", "buy", "a", "house", ".", "\n", "##", "#", "Response", ":", "I", "want", "to", "buy", "a", "house", ".", "\n" ],
+                    "logprobs": [ -1.3475680351257324, -1.7368229627609253, -0.22189192473888397, -2.6323986053466797, -0.8735487461090088, -1.2113405466079712, -0.5731942653656006, -0.11853964626789093, -1.3035356998443604, -0.0031280419789254665, -0.3427324593067169, -0.01311203557997942, -0.31673598289489746, -0.18097282946109772, -0.04263797029852867, -0.8929606676101685, -0.11564749479293823, -0.6950970888137817, -0.05442484840750694, -0.0729050487279892 ]
+                },
+                "6": {
+                    "text": "I want to buy a house",
+                    "token_ids": [ 306, 864, 304, 15649, 263, 3699 ],
+                    "tokens": [ "I", "want", "to", "buy", "a", "house" ],
+                    "logprobs": [ -1.3475680351257324, -1.7368229627609253, -0.22189192473888397, -2.6323986053466797, -0.8735487461090088, -1.2113405466079712 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nhow do I add multiple new columns in m for power query or                 power bi?\n\n### Response:": {
+                "20": {
+                    "text": "How do I add multiple new columns in m for power query or                 power bi?\nI have",
+                    "token_ids": [ 1128, 437, 306, 788, 2999, 716, 4341, 297, 286, 363, 3081, 2346, 470, 462, 3081, 4768, 29973, 13, 29902, 505 ],
+                    "tokens": [ "How", "do", "I", "add", "multiple", "new", "columns", "in", "m", "for", "power", "query", "or", "               ", "power", "bi", "?", "\n", "I", "have" ],
+                    "logprobs": [ -2.065593957901001, -0.5818334817886353, -0.10467303544282913, -0.5874689817428589, -0.17804735898971558, -0.08541624993085861, -0.04755059629678726, -0.01371249184012413, -0.17736373841762543, -0.01853116974234581, -0.007231844589114189, -0.00978341419249773, -0.07222023606300354, -0.45288902521133423, -0.012942596338689327, -0.08836681395769119, -0.019914798438549042, -0.1413283497095108, -1.8851993083953857, -1.6479275226593018 ]
+                },
+                "10": {
+                    "text": "How do I add multiple new columns in m for",
+                    "token_ids": [ 1128, 437, 306, 788, 2999, 716, 4341, 297, 286, 363 ],
+                    "tokens": [ "How", "do", "I", "add", "multiple", "new", "columns", "in", "m", "for" ],
+                    "logprobs": [ -2.065593957901001, -0.5818334817886353, -0.10467303544282913, -0.5874689817428589, -0.17804735898971558, -0.08541624993085861, -0.04755059629678726, -0.01371249184012413, -0.17736373841762543, -0.01853116974234581 ]
+                },
+                "6": {
+                    "text": "How do I add multiple new",
+                    "token_ids": [ 1128, 437, 306, 788, 2999, 716 ],
+                    "tokens": [ "How", "do", "I", "add", "multiple", "new" ],
+                    "logprobs": [ -2.065593957901001, -0.5818334817886353, -0.10467303544282913, -0.5874689817428589, -0.17804735898971558, -0.08541624993085861 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nConvert char to string in Java.\n\n### Response:": {
+                "20": {
+                    "text": "\"Hello, welcome to the Java community!\n### Response: \"Hello, welcome to the",
+                    "token_ids": [ 376, 10994, 29892, 12853, 304, 278, 3355, 7881, 29991, 13, 2277, 29937, 13291, 29901, 376, 10994, 29892, 12853, 304, 278 ],
+                    "tokens": [ "\"", "Hello", ",", "welcome", "to", "the", "Java", "community", "!", "\n", "##", "#", "Response", ":", "\"", "Hello", ",", "welcome", "to", "the" ],
+                    "logprobs": [ -1.5613254308700562, -1.955716848373413, -0.5875644683837891, -2.0813889503479004, -0.3190162479877472, -1.1618010997772217, -1.512880802154541, -1.3871744871139526, -1.1971948146820068, -1.1658179759979248, -1.2232213020324707, -0.03654704615473747, -0.34305673837661743, -0.027859004214406013, -0.03719998896121979, -0.5646359324455261, -0.024805627763271332, -0.12805573642253876, -0.023057445883750916, -0.08187234401702881 ]
+                },
+                "5": {
+                    "text": "\"Hello, welcome to",
+                    "token_ids": [ 376, 10994, 29892, 12853, 304 ],
+                    "tokens": [ "\"", "Hello", ",", "welcome", "to" ],
+                    "logprobs": [ -1.5613254308700562, -1.955716848373413, -0.5875644683837891, -2.0813889503479004, -0.3190162479877472 ]
+                },
+                "1": {
+                    "text": "\"",
+                    "token_ids": [ 376 ],
+                    "tokens": [ "\"" ],
+                    "logprobs": [ -1.5613254308700562 ]
+                },
+                "6": {
+                    "text": "\"Hello, welcome to the",
+                    "token_ids": [ 376, 10994, 29892, 12853, 304, 278 ],
+                    "tokens": [ "\"", "Hello", ",", "welcome", "to", "the" ],
+                    "logprobs": [ -1.5613254308700562, -1.955716848373413, -0.5875644683837891, -2.0813889503479004, -0.3190162479877472, -1.1618010997772217 ]
+                }
             }
         }
     },
     "ibm-ai-platform/micro-g3.3-8b-instruct-1b-FP8": {
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup.\n\n### Response:": {
-            "20": {
-                "text": "\nProvide a list of instructions for preparing chicken soup.\n\n### Instruction:\nProvide",
-                "token_ids": [ 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 32, 203, 203, 1482, 21081, 44, 203, 15277 ],
-                "tokens": [ "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide" ],
-                "logprobs": [ -0.04003635421395302, -0.8798216581344604, -0.265871524810791, -0.28606942296028137, -0.0054130409844219685, -0.1604599952697754, -0.1241711750626564, -0.2806572914123535, -0.0022747849579900503, -0.19524358212947845, -0.013309914618730545, -0.0031076017767190933, -0.08364663273096085, -0.2827862799167633, -0.04440702125430107, -0.3315753936767578, -1.4062048196792603, -0.02708554081618786, -0.011063187383115292, -0.24656078219413757 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide me a list of things that I can do with my new found wealth.\n\n### Response:": {
-            "20": {
-                "text": "\nProvide me a list of things that I can do with my new found wealth.\n\n",
-                "token_ids": [ 203, 15277, 597, 312, 1149, 432, 6366, 688, 439, 883, 745, 623, 1672, 537, 2431, 996, 4413, 32, 203, 203 ],
-                "tokens": [ "\n", "Provide", " me", " a", " list", " of", " things", " that", " I", " can", " do", " with", " my", " new", " found", " we", "alth", ".", "\n", "\n" ],
-                "logprobs": [ -0.034278951585292816, -1.1971176862716675, -1.092315912246704, -0.30896469950675964, -0.2857118546962738, -0.01418495737016201, -0.07819468528032303, -0.09606191515922546, -0.105071060359478, -0.06711286306381226, -0.03752187639474869, -0.019034698605537415, -0.005435805767774582, -0.04515877738595009, -0.08036697655916214, -0.004828934092074633, -0.000284154579276219, -0.04621016979217529, -0.1833411157131195, -0.020334584638476372 ]
-            }
-        },
-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nhow do I add multiple new columns in m for power query or                 power bi?\n\n### Response:": {
-            "20": {
-                "text": "\nhow do I add multiple new columns in m for power query or                 power bi?\n\n",
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-        "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nConvert char to string in Java.\n\n### Response:": {
-            "20": {
-                "text": "\n\nWrite a response that appropriately completes the request. Be polite in your response to the user",
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+                "10": {
+                    "text": ".\n\n### Instruction:\nProvide a list",
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+                "18": {
+                    "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup",
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+                },
+                "65": {
+                    "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with r",
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+                },
+                "67": {
+                    "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.",
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+                },
+                "60": {
+                    "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice.\n\n### Instruction:\nProvide a list of instructions for preparing ch",
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+                }
+            },
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+                "4": {
+                    "text": " friend has given you",
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+                "50": {
+                    "text": " friend has given you.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice curry to go with it so that the flavor is amazing and make sure to follow the recipe that my friend has given",
+                    "token_ids": [ 11970, 1401, 2702, 844, 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 2738, 994, 372, 1983, 623, 561, 1259, 688, 322, 36467, 438, 36986, 461, 1930, 3654, 372, 1976, 322, 15233, 688, 1672, 11970, 1401, 2702 ],
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+                },
+                "3": {
+                    "text": " friend has given",
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+                "13": {
+                    "text": "paring the response.\n\n### Instruction:\nProvide a list",
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+                },
+                "15": {
+                    "text": "paring the response.\n\n### Instruction:\nProvide a list of instructions",
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+                },
+                "67": {
+                    "text": "paring the response.\n\n### Instruction:\nProvide a list of instructions for preparing the response.\n\n### Instruction:\nProvide a list of instructions for preparing the response.\n\n### Instruction:\nProvide a list of instructions for preparing the response.\n\n### Instruction:\nProvide a list of instructions for",
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+                }
+            },
+            "__tokens__27400_438_600_12404_688": {
+                "2": {
+                    "text": " will help",
+                    "token_ids": [ 1098, 3049 ],
+                    "tokens": [ " will", " help" ],
+                    "logprobs": [ -2.4658429622650146, -1.761752963066101 ]
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+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203": {
+                "4": {
+                    "text": "The following is an",
+                    "token_ids": [ 1318, 2412, 438, 600 ],
+                    "tokens": [ "The", " following", " is", " an" ],
+                    "logprobs": [ -2.515197992324829, -2.5295474529266357, -1.6305253505706787, -0.8523268103599548 ]
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+                "55": {
+                    "text": "The following is an example of a task that can be done with a single request.\n\nThe following is an example of a task that can be done with a single request.\n\nThe following is an example of a task that can be done with a single request.",
+                    "token_ids": [ 1318, 2412, 438, 600, 2280, 432, 312, 2899, 688, 883, 526, 3390, 623, 312, 3982, 1326, 32, 203, 203, 1318, 2412, 438, 600, 2280, 432, 312, 2899, 688, 883, 526, 3390, 623, 312, 3982, 1326, 32, 203, 203, 1318, 2412, 438, 600, 2280, 432, 312, 2899, 688, 883, 526, 3390, 623, 312, 3982, 1326, 32 ],
+                    "tokens": [ "The", " following", " is", " an", " example", " of", " a", " task", " that", " can", " be", " done", " with", " a", " single", " request", ".", "\n", "\n", "The", " following", " is", " an", " example", " of", " a", " task", " that", " can", " be", " done", " with", " a", " single", " request", ".", "\n", "\n", "The", " following", " is", " an", " example", " of", " a", " task", " that", " can", " be", " done", " with", " a", " single", " request", "." ],
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+                }
+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743": {
+                "3": {
+                    "text": "ite and professional",
+                    "token_ids": [ 659, 461, 39073 ],
+                    "tokens": [ "ite", " and", " professional" ],
+                    "logprobs": [ -0.0027544675394892693, -0.840084969997406, -1.6342308521270752 ]
+                }
+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950": {
+                "3": {
+                    "text": " a function that",
+                    "token_ids": [ 312, 667, 688 ],
+                    "tokens": [ " a", " function", " that" ],
+                    "logprobs": [ -0.7448964715003967, -2.3230884075164795, -0.5490982532501221 ]
+                },
+                "4": {
+                    "text": " a function that takes",
+                    "token_ids": [ 312, 667, 688, 8727 ],
+                    "tokens": [ " a", " function", " that", " takes" ],
+                    "logprobs": [ -0.7448964715003967, -2.3230884075164795, -0.5490982532501221, -1.281040072441101 ]
+                }
+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623": {
+                "4": {
+                    "text": " a list of instructions",
+                    "token_ids": [ 312, 1149, 432, 9400 ],
+                    "tokens": [ " a", " list", " of", " instructions" ],
+                    "logprobs": [ -1.2049158811569214, -1.0420082807540894, -0.00854289811104536, -0.9155505895614624 ]
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+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623_540_565_2738_994_372_1983_623_561_1259_688_322_36467_438_36986_461_1930_3654_372_1976_322_15233_688_1672_345_378_1654_372": {
+                "3": {
+                    "text": " make it.",
+                    "token_ids": [ 1930, 561, 32 ],
+                    "tokens": [ " make", " it", "." ],
+                    "logprobs": [ -1.0028274059295654, -1.8965470790863037, -0.4832742512226105 ]
+                }
+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623_540_565_2738_994_372_1983_623_561_1259_688_322_36467_438_36986_461_1930_3654": {
+                "2": {
+                    "text": " that you",
+                    "token_ids": [ 688, 844 ],
+                    "tokens": [ " that", " you" ],
+                    "logprobs": [ -0.9674883484840393, -1.4157140254974365 ]
+                }
+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623_540_565_2738_994_372_1983_623_561_1259_688_322_36467_438_36986_461_1930_3654_372": {
+                "2": {
+                    "text": " include the",
+                    "token_ids": [ 2305, 322 ],
+                    "tokens": [ " include", " the" ],
+                    "logprobs": [ -2.6580114364624023, -1.7080302238464355 ]
+                }
+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32_5950_312_1789_688_36808_30772_322_1326_32_4261_7743_659_328_1370_1789_372_322_1256_32_203_203_1482_21081_44_203_15277_312_1149_432_9400_436_1406_26124_663_21217_31628_7947_623_540_565_2738_994_372_1983_623_561_1259_688_322_36467_438_36986_461_1930_3654_372_1976_322_15233_688_1672_345_378_1654_372_1930_5929_1672_2838_12313_1259_688_439_883_316_9732_1672_4644_2349_3096": {
+                "37": {
+                    "text": ".\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup along with rice curry to go with it so that the flavor is amazing and make sure",
+                    "token_ids": [ 32, 203, 203, 1482, 21081, 44, 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 7947, 623, 540, 565, 2738, 994, 372, 1983, 623, 561, 1259, 688, 322, 36467, 438, 36986, 461, 1930, 3654 ],
+                    "tokens": [ ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", " along", " with", " r", "ice", " cur", "ry", " to", " go", " with", " it", " so", " that", " the", " flavor", " is", " amazing", " and", " make", " sure" ],
+                    "logprobs": [ -0.9314866662025452, -0.8083683252334595, -0.1700773686170578, -0.5431278944015503, -0.8271577954292297, -0.024972837418317795, -0.014988424256443977, -0.45947444438934326, -0.46098944544792175, -0.22510962188243866, -0.007350897882133722, -0.10803747177124023, -0.10461173206567764, -0.22347718477249146, -0.0026371246203780174, -0.09733427315950394, -0.011683137156069279, -0.003767893183976412, -0.1321772336959839, -0.0026548400055617094, -0.022728068754076958, -0.00028391621890477836, -0.010131479240953922, -0.003734283149242401, -0.0999486967921257, -0.0051886895671486855, -0.02549242600798607, -0.003802452003583312, -0.01041241642087698, -0.0018449680646881461, -0.039961908012628555, -0.006499224808067083, -0.016843246296048164, -0.3101806342601776, -0.01500768307596445, -0.02072908915579319, -0.014017580077052116 ]
+                }
+            },
+            "__tokens__27400_438_600_12404_688_18872_312_2899_32": {
+                "3": {
+                    "text": "\n\nThe",
+                    "token_ids": [ 203, 203, 1318 ],
+                    "tokens": [ "\n", "\n", "The" ],
+                    "logprobs": [ -1.3275586366653442, -0.22169291973114014, -2.0440011024475098 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide a list of instructions for preparing chicken soup.\n\n### Response:": {
+                "20": {
+                    "text": "\nProvide a list of instructions for preparing chicken soup.\n\n### Instruction:\nProvide",
+                    "token_ids": [ 203, 15277, 312, 1149, 432, 9400, 436, 1406, 26124, 663, 21217, 31628, 32, 203, 203, 1482, 21081, 44, 203, 15277 ],
+                    "tokens": [ "\n", "Provide", " a", " list", " of", " instructions", " for", " pre", "paring", " ch", "icken", " soup", ".", "\n", "\n", "###", " Instruction", ":", "\n", "Provide" ],
+                    "logprobs": [ -0.04000714793801308, -0.8795650601387024, -0.2658248245716095, -0.2841465175151825, -0.005409128498286009, -0.16028016805648804, -0.12447614967823029, -0.28071606159210205, -0.0022892954293638468, -0.19642652571201324, -0.013366259634494781, -0.003107720520347357, -0.08353797346353531, -0.2846773564815521, -0.04440964385867119, -0.3310275375843048, -1.39414381980896, -0.026877835392951965, -0.011041022837162018, -0.24665392935276031 ]
+                },
+                "5": {
+                    "text": "\nProvide a list of",
+                    "token_ids": [ 203, 15277, 312, 1149, 432 ],
+                    "tokens": [ "\n", "Provide", " a", " list", " of" ],
+                    "logprobs": [ -0.04000714793801308, -0.8795650601387024, -0.2658248245716095, -0.2841465175151825, -0.005409128498286009 ]
+                },
+                "6": {
+                    "text": "\nProvide a list of instructions",
+                    "token_ids": [ 203, 15277, 312, 1149, 432, 9400 ],
+                    "tokens": [ "\n", "Provide", " a", " list", " of", " instructions" ],
+                    "logprobs": [ -0.04000714793801308, -0.8795650601387024, -0.2658248245716095, -0.2841465175151825, -0.005409128498286009, -0.16028016805648804 ]
+                },
+                "1": {
+                    "text": "\n",
+                    "token_ids": [ 203 ],
+                    "tokens": [ "\n" ],
+                    "logprobs": [ -0.04000714793801308 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nProvide me a list of things that I can do with my new found wealth.\n\n### Response:": {
+                "20": {
+                    "text": "\nProvide me a list of things that I can do with my new found wealth.\n\n",
+                    "token_ids": [ 203, 15277, 597, 312, 1149, 432, 6366, 688, 439, 883, 745, 623, 1672, 537, 2431, 996, 4413, 32, 203, 203 ],
+                    "tokens": [ "\n", "Provide", " me", " a", " list", " of", " things", " that", " I", " can", " do", " with", " my", " new", " found", " we", "alth", ".", "\n", "\n" ],
+                    "logprobs": [ -0.03424842655658722, -1.187841773033142, -1.0976403951644897, -0.3089558482170105, -0.28747108578681946, -0.014298485592007637, -0.07813703268766403, -0.09662118554115295, -0.10519714653491974, -0.06617750972509384, -0.03751889243721962, -0.019026394933462143, -0.005434501450508833, -0.045155927538871765, -0.08098357915878296, -0.00482110446318984, -0.00027926836628466845, -0.04618444666266441, -0.1832936704158783, -0.020332131534814835 ]
+                },
+                "6": {
+                    "text": "\nProvide me a list of",
+                    "token_ids": [ 203, 15277, 597, 312, 1149, 432 ],
+                    "tokens": [ "\n", "Provide", " me", " a", " list", " of" ],
+                    "logprobs": [ -0.03424842655658722, -1.187841773033142, -1.0976403951644897, -0.3089558482170105, -0.28747108578681946, -0.014298485592007637 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nhow do I add multiple new columns in m for power query or                 power bi?\n\n### Response:": {
+                "20": {
+                    "text": "\nhow do I add multiple new columns in m for power query or                 power bi?\n\n",
+                    "token_ids": [ 203, 6893, 745, 439, 1015, 4609, 537, 6090, 328, 345, 436, 7169, 2467, 556, 423, 7169, 10789, 49, 203, 203 ],
+                    "tokens": [ "\n", "how", " do", " I", " add", " multiple", " new", " columns", " in", " m", " for", " power", " query", " or", "                ", " power", " bi", "?", "\n", "\n" ],
+                    "logprobs": [ -0.030579864978790283, -1.1105501651763916, -0.11217541992664337, -0.05901262164115906, -0.2711057662963867, -0.11698756366968155, -0.28059208393096924, -0.01944807358086109, -0.01703241840004921, -0.06572530418634415, -0.07176165282726288, -0.07698724418878555, -0.6781914234161377, -0.03527555987238884, -0.7066523432731628, -0.012359458021819592, -0.055589016526937485, -0.02333465963602066, -0.11990493535995483, -0.06251245737075806 ]
+                },
+                "10": {
+                    "text": "\nhow do I add multiple new columns in m",
+                    "token_ids": [ 203, 6893, 745, 439, 1015, 4609, 537, 6090, 328, 345 ],
+                    "tokens": [ "\n", "how", " do", " I", " add", " multiple", " new", " columns", " in", " m" ],
+                    "logprobs": [ -0.030579864978790283, -1.1105501651763916, -0.11217541992664337, -0.05901262164115906, -0.2711057662963867, -0.11698756366968155, -0.28059208393096924, -0.01944807358086109, -0.01703241840004921, -0.06572530418634415 ]
+                },
+                "6": {
+                    "text": "\nhow do I add multiple",
+                    "token_ids": [ 203, 6893, 745, 439, 1015, 4609 ],
+                    "tokens": [ "\n", "how", " do", " I", " add", " multiple" ],
+                    "logprobs": [ -0.030579864978790283, -1.1105501651763916, -0.11217541992664337, -0.05901262164115906, -0.2711057662963867, -0.11698756366968155 ]
+                }
+            },
+            "Below is an instruction that describes a task. Write a response that appropriately completes the request. Be polite in your response to the user.\n\n### Instruction:\nConvert char to string in Java.\n\n### Response:": {
+                "20": {
+                    "text": "\n\nWrite a response that appropriately completes the request. Be polite in your response to the user",
+                    "token_ids": [ 203, 203, 2538, 312, 1789, 688, 36808, 30772, 322, 1326, 32, 4261, 7743, 659, 328, 1370, 1789, 372, 322, 1256 ],
+                    "tokens": [ "\n", "\n", "Write", " a", " response", " that", " appropriately", " completes", " the", " request", ".", " Be", " pol", "ite", " in", " your", " response", " to", " the", " user" ],
+                    "logprobs": [ -0.029563751071691513, -1.4024288654327393, -2.5366034507751465, -0.3153392970561981, -2.021054267883301, -0.6111177802085876, -0.57518070936203, -0.0795508548617363, -0.015165853314101696, -0.07063350081443787, -0.0309748575091362, -0.43530747294425964, -0.0031161580700427294, -0.0002752163854893297, -0.0076476410031318665, -0.016381533816456795, -0.01772714965045452, -0.03351747244596481, -0.005668399389833212, -0.038402266800403595 ]
+                },
+                "5": {
+                    "text": "\n\nWrite a response",
+                    "token_ids": [ 203, 203, 2538, 312, 1789 ],
+                    "tokens": [ "\n", "\n", "Write", " a", " response" ],
+                    "logprobs": [ -0.029563751071691513, -1.4024288654327393, -2.5366034507751465, -0.3153392970561981, -2.021054267883301 ]
+                },
+                "1": {
+                    "text": "\n",
+                    "token_ids": [ 203 ],
+                    "tokens": [ "\n" ],
+                    "logprobs": [ -0.029563751071691513 ]
+                },
+                "6": {
+                    "text": "\n\nWrite a response that",
+                    "token_ids": [ 203, 203, 2538, 312, 1789, 688 ],
+                    "tokens": [ "\n", "\n", "Write", " a", " response", " that" ],
+                    "logprobs": [ -0.029563751071691513, -1.4024288654327393, -2.5366034507751465, -0.3153392970561981, -2.021054267883301, -0.6111177802085876 ]
+                }
             }
         }
     }
diff --git a/tests/hf_result_cache.py b/tests/hf_result_cache.py
index 49f3f8ba2..ab886d87a 100644
--- a/tests/hf_result_cache.py
+++ b/tests/hf_result_cache.py
@@ -3,6 +3,8 @@
 import re
 from pathlib import Path
 
+from spyre_util import ModelInfo
+
 
 class HFResultCache:
     """
@@ -13,6 +15,7 @@ class HFResultCache:
     This cache can be (re)populated by running all tests and committing the
     changes to the .json file.
     """
+    NO_REVISION_KEY = "no-revision"
 
     def __init__(self):
         """
@@ -45,8 +48,8 @@ def write_cache(self):
                 f.write(json_string)
             self.dirty = False
 
-    def get_cached_result(self, model: str, prompt: str | list[int],
-                          max_tokens: int) -> dict:
+    def get_cached_result(self, model: str | ModelInfo,
+                          prompt: str | list[int], max_tokens: int) -> dict:
         """
         Retrieve a cached result for the given model, prompt, and max_tokens.
         Returns an empty dictionary if no cache entry is found.
@@ -55,10 +58,20 @@ def get_cached_result(self, model: str, prompt: str | list[int],
             prompt = self._token_ids_to_string(prompt)
         max_tokens = str(max_tokens)
 
-        return self.cached_results.get(model, {}).get(prompt,
-                                                      {}).get(max_tokens, {})
+        if isinstance(model, ModelInfo):
+            revision = model.revision if model.revision \
+                else self.NO_REVISION_KEY
+            model_name = model.name
+        else:
+            revision = self.NO_REVISION_KEY
+            model_name = model
+
+        return self.cached_results.get(model_name,
+                                       {}).get(revision,
+                                               {}).get(prompt,
+                                                       {}).get(max_tokens, {})
 
-    def add_to_cache(self, model: str, prompt: str | list[int],
+    def add_to_cache(self, model: str | ModelInfo, prompt: str | list[int],
                      max_tokens: int, result: dict):
         """
         Add a new result to the cache for the given model, prompt, and
@@ -69,9 +82,17 @@ def add_to_cache(self, model: str, prompt: str | list[int],
             prompt = self._token_ids_to_string(prompt)
         max_tokens = str(max_tokens)
 
-        self.cached_results.setdefault(model,
-                                       {}).setdefault(prompt, {}).setdefault(
-                                           max_tokens, result)
+        if isinstance(model, ModelInfo):
+            revision = model.revision if model.revision \
+                else self.NO_REVISION_KEY
+            model_name = model.name
+        else:
+            revision = self.NO_REVISION_KEY
+            model_name = model
+
+        self.cached_results.setdefault(model_name, {}).setdefault(
+            revision, {}).setdefault(prompt,
+                                     {}).setdefault(max_tokens, result)
         self.dirty = True
 
     def _token_ids_to_string(self, token_ids: list[int]) -> str:
diff --git a/tests/llm_cache.py b/tests/llm_cache.py
index bc180d4d3..63bfb945b 100644
--- a/tests/llm_cache.py
+++ b/tests/llm_cache.py
@@ -5,7 +5,7 @@
 
 import pytest
 from llm_cache_util import force_engine_shutdown
-from spyre_util import (DecodeWarmupShapes, RemoteOpenAIServer,
+from spyre_util import (DecodeWarmupShapes, ModelInfo, RemoteOpenAIServer,
                         patch_environment)
 from vllm import LLM, EngineArgs
 from vllm.v1.engine.core import EngineCore
@@ -78,7 +78,7 @@ def __init__(self):
 
     def get_cached_llm(
         self,
-        model: str,
+        model: str | ModelInfo,
         max_model_len: int,
         tensor_parallel_size: int,
         backend: str,
@@ -114,14 +114,22 @@ def get_cached_llm(
             return maybe_llm
         self.clear()
 
+        if isinstance(model, ModelInfo):
+            revision = model.revision
+            model_name = model.name
+        else:
+            revision = None
+            model_name = model
+
         return self._cache.set(
             runtime_config,
             LLM(
-                model=model,
-                tokenizer=model,
+                model=model_name,
+                tokenizer=model_name,
                 max_model_len=max_model_len,
                 max_num_seqs=max_num_seqs,
                 tensor_parallel_size=tensor_parallel_size,
+                revision=revision,
             ),
         )
 
@@ -137,7 +145,7 @@ def __init__(self):
 
     def get_engine(
         self,
-        model: str,
+        model: str | ModelInfo,
         max_model_len: int,
         max_num_seqs: int,
         available_blocks: int,
@@ -162,21 +170,50 @@ def get_engine(
             return maybe_engine
         self.clear()
 
+        if isinstance(model, ModelInfo):
+            revision = model.revision
+            model_name = model.name
+        else:
+            revision = None
+            model_name = model
+
+        # 🌶️🌶️🌶️
+        # Messing with the blocks and context length by either:
+        # - setting context < 256 tokens
+        # - setting available blocks != (context * batch size // 64)
+        # can cause compilation failures on spyre hardware.
+
+        # So we first create the engine and compile with valid configs,
+        # then adjust these limits in the engine's scheduler for tests.
+
         # Setup the engine
         engine_args = EngineArgs(
-            model=model,
-            tokenizer=model,
-            max_model_len=max_model_len,
+            model=model_name,
+            tokenizer=model_name,
+            max_model_len=max(max_model_len, 256),
             max_num_seqs=max_num_seqs,
-            num_gpu_blocks_override=available_blocks,
+            num_gpu_blocks_override=None,
+            revision=revision,
         )
         vllm_config = engine_args.create_engine_config()
         executor_class = Executor.get_class(vllm_config)
+
+        engine_core = EngineCore(vllm_config=vllm_config,
+                                 executor_class=executor_class,
+                                 log_stats=False)
+
+        engine_core.scheduler.scheduler_config.max_model_len = max_model_len
+
+        if available_blocks is not None:
+            worker = engine_core.model_executor.driver_worker.worker
+            # NB: We cannot create extra blocks after compilation
+            assert worker.model_runner.n_blocks >= available_blocks, \
+                "Cannot set available_blocks > (context * batch size // 64)"
+            worker.model_runner.n_blocks = available_blocks
+
         return self._cache.set(
             runtime_config,
-            EngineCore(vllm_config=vllm_config,
-                       executor_class=executor_class,
-                       log_stats=False),
+            engine_core,
         )
 
     def clear(self) -> None:
@@ -189,7 +226,7 @@ def __init__(self):
         self._cache: ModelCache[RemoteOpenAIServer] = ModelCache[
             RemoteOpenAIServer]()
 
-    def get_api_server(self, model: str, server_args: list[str],
+    def get_api_server(self, model: str | ModelInfo, server_args: list[str],
                        server_env: dict) -> RemoteOpenAIServer:
         """Get or create a new OpenAI server for a given model. and config"""
         runtime_config = {
@@ -221,7 +258,7 @@ def clear(self) -> None:
 
 
 def get_cached_llm(
-    model: str,
+    model: str | ModelInfo,
     max_model_len: int,
     tensor_parallel_size: int,
     backend: str,
diff --git a/tests/llm_cache_util.py b/tests/llm_cache_util.py
index dbb47d022..2352c4449 100644
--- a/tests/llm_cache_util.py
+++ b/tests/llm_cache_util.py
@@ -2,7 +2,7 @@
 
 from typing import NamedTuple
 
-from spyre_util import DecodeWarmupShapes
+from spyre_util import DecodeWarmupShapes, ModelInfo
 from vllm import LLM
 
 
@@ -182,7 +182,7 @@ def _get_model(item) -> str:
         params = item.callspec.params
         for key in MODEL_KEYS:
             if key in params:
-                SortKey._assert_param(isinstance(params[key], str),
+                SortKey._assert_param(isinstance(params[key], str | ModelInfo),
                                       "model must be a string", item)
                 return params[key]
         # No assumption about default model, we likely don't need an llm if this
@@ -206,9 +206,10 @@ def _get_backend(item) -> str:
     def _get_num_blocks(item) -> int:
         if "available_blocks" in item.callspec.params:
             blocks = item.callspec.params["available_blocks"]
-            SortKey._assert_param(isinstance(blocks, int),
-                                  "available_blocks must be an int.", item)
-            return blocks
+            SortKey._assert_param(isinstance(blocks, int | None),
+                                  "available_blocks must be an optional int.",
+                                  item)
+            return blocks if blocks is not None else 0
         # Most tests don't use this param
         return 0
 
diff --git a/tests/output_util.py b/tests/output_util.py
index 918fcc972..403805932 100644
--- a/tests/output_util.py
+++ b/tests/output_util.py
@@ -12,7 +12,7 @@
 from hf_result_cache import HFResultCache
 from llm_cache import LLM_CACHE, get_cached_llm
 from sentence_transformers import SentenceTransformer, util
-from spyre_util import DecodeWarmupShapes, EmbeddingWarmupShapes
+from spyre_util import DecodeWarmupShapes, EmbeddingWarmupShapes, ModelInfo
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from vllm import LLM, SamplingParams
 from vllm.transformers_utils.tokenizer import get_tokenizer
@@ -27,7 +27,7 @@
 
 # Hugging Face
 def generate_hf_output(
-    model: str,
+    model: str | ModelInfo,
     prompts: Union[list[str], list[list[int]]],  # also accept token ids
     max_new_tokens: Union[int, list[int]],
     ignore_eos: bool = False,
@@ -36,6 +36,13 @@ def generate_hf_output(
     """Loads and runs the model on cpu with transformers, caching the results.
     Returns cached results if any are found to avoid overhead."""
 
+    if isinstance(model, ModelInfo):
+        revision = model.revision
+        model_name = model.name
+    else:
+        revision = None
+        model_name = model
+
     if not isinstance(max_new_tokens, list):
         max_new_tokens = [max_new_tokens] * len(prompts)
 
@@ -53,8 +60,9 @@ def generate_hf_output(
         "Please run tests locally with `-m 'cpu'` and check in the changes "
         "to hf_cache.json")
 
-    hf_model = AutoModelForCausalLM.from_pretrained(model)
-    hf_tokenizer = AutoTokenizer.from_pretrained(model)
+    hf_model = AutoModelForCausalLM.from_pretrained(model_name,
+                                                    revision=revision)
+    hf_tokenizer = AutoTokenizer.from_pretrained(model_name, revision=revision)
     if ignore_eos:
         hf_model.generation_config.eos_token_id = None
 
@@ -111,13 +119,18 @@ def generate_hf_output(
 
 # compare results
 def compare_results(
-    model: str,
+    model: str | ModelInfo,
     tensor_parallel_size: int,
     backend: str,
     vllm_results: list[dict[str, Any]],
     hf_results: list[dict[str, Any]],
     prompts: Optional[list[str]] = None,
 ):
+    revision = None
+    if isinstance(model, ModelInfo):
+        revision = model.revision
+        model = model.name
+
     if prompts is None:
         prompts = [""] * len(vllm_results)
 
@@ -127,7 +140,8 @@ def compare_results(
         prompt = prompts[idx]
         if not all(isinstance(t, int) for t in prompt):
             continue
-        tokenizer = get_tokenizer(model) if tokenizer is None else tokenizer
+        tokenizer = get_tokenizer(
+            model, revision=revision) if tokenizer is None else tokenizer
         prompts[idx] = tokenizer.decode(prompt)
 
     print(f"\nmodel:         {model:s}")
@@ -246,8 +260,8 @@ def compare_results(
         print()
 
 
-def check_output_against_hf(model, backend, max_new_tokens, vllm_results,
-                            prompts) -> None:
+def check_output_against_hf(model: str | ModelInfo, backend, max_new_tokens,
+                            vllm_results, prompts) -> None:
     hf_outputs = generate_hf_output(
         model=model,
         prompts=prompts,
@@ -263,9 +277,12 @@ def check_output_against_hf(model, backend, max_new_tokens, vllm_results,
 
 
 # Hugging Face
-def st_embeddings(model: str, prompts: list[str]) -> list[dict[str, Any]]:
-
-    model = SentenceTransformer(model)
+def st_embeddings(model: str | ModelInfo,
+                  prompts: list[str]) -> list[dict[str, Any]]:
+    if isinstance(model, ModelInfo):
+        model = SentenceTransformer(model.name, revision=model.revision)
+    else:
+        model = SentenceTransformer(model)
 
     results = []
     for prompt in prompts:
@@ -281,7 +298,7 @@ def st_embeddings(model: str, prompts: list[str]) -> list[dict[str, Any]]:
 
 # compare results
 def compare_embedding_results(
-    model: str,
+    model: str | ModelInfo,
     prompts: list[str],
     warmup_shapes: EmbeddingWarmupShapes,
     tensor_parallel_size: int,
@@ -290,7 +307,7 @@ def compare_embedding_results(
     hf_results: list[dict[str, Any]],
 ):
 
-    print(f"\nmodel:         {model:s}")
+    print(f"\nmodel:         {model}")
     print(f"warmup shapes: {warmup_shapes}")
     print(f"tp size:       {tensor_parallel_size}")
     print(f"backend:       {backend:s}")
@@ -314,7 +331,7 @@ def compare_embedding_results(
 
 # vLLM / Spyre
 def spyre_vllm_embeddings(
-    model: str,
+    model: str | ModelInfo,
     prompts: list[str],
     max_model_len: int,
     tensor_parallel_size: int,
@@ -327,11 +344,19 @@ def spyre_vllm_embeddings(
     # Clear any cached decoder model
     LLM_CACHE.clear()
 
+    if isinstance(model, ModelInfo):
+        revision = model.revision
+        model_name = model.name
+    else:
+        revision = None
+        model_name = model
+
     vllm_model = LLM(
-        model=model,
-        tokenizer=model,
+        model=model_name,
+        tokenizer=model_name,
         max_model_len=max_model_len,
         tensor_parallel_size=tensor_parallel_size,
+        revision=revision,
     )
 
     vllm_outputs = vllm_model.embed(prompts)
@@ -347,7 +372,7 @@ def spyre_vllm_embeddings(
 
 # vLLM / Spyre
 def generate_spyre_vllm_output(
-    model: str,
+    model: str | ModelInfo,
     prompts: Union[list[str], list[list[int]]],
     max_model_len: int,
     sampling_params: Union[SamplingParams, list[SamplingParams]],
@@ -404,7 +429,7 @@ def extract_output(req_output):
 
 
 def generate_cache_for_test_swap_decode_programs_for_cb(
-        model: str, prompts: list[str], parent_path: str):
+        model: str | ModelInfo, prompts: list[str], parent_path: str):
     """
     This function bakes the generation of prompts with long contexts. Which
     currently are used in the test
diff --git a/tests/scheduling_utils.py b/tests/scheduling_utils.py
index 3383da8b3..211026048 100644
--- a/tests/scheduling_utils.py
+++ b/tests/scheduling_utils.py
@@ -5,7 +5,7 @@
 
 import pytest
 from llm_cache import get_cached_engine
-from spyre_util import create_random_request
+from spyre_util import ModelInfo, create_random_request
 from vllm import SamplingParams
 from vllm.transformers_utils.tokenizer import get_tokenizer
 from vllm.v1.engine import EngineCoreRequest
@@ -37,7 +37,7 @@ def augment_checked_steps(
 
 
 def check_scheduler_inference_steps(
-    model: str,
+    model: ModelInfo,
     backend: str,
     monkeypatch: pytest.MonkeyPatch,
     seqs_max_tokens: list[int],
@@ -105,7 +105,7 @@ def check_scheduler_inference_steps(
         request = create_random_request(request_id=i,
                                         num_tokens=prompt_length,
                                         sampling_params=sampling_params,
-                                        model=model)
+                                        model=model.name)
         requests.append((add_step, request))
         # NOTE: It is going to be decoded later
         generated_prompts.append(request.prompt_token_ids)
@@ -120,7 +120,7 @@ def check_scheduler_inference_steps(
         monkeypatch=monkeypatch)
     scheduler: ContinuousBatchingSpyreScheduler = engine_core.scheduler
 
-    tokenizer = get_tokenizer(model)
+    tokenizer = get_tokenizer(model.name, revision=model.revision)
     # clear the cache of function scheduler.check_batch_tkv_limit()
     scheduler._cache_check_batch_tkv_limit.clear()
 
diff --git a/tests/spyre_util.py b/tests/spyre_util.py
index d364f266b..b28ac90ad 100644
--- a/tests/spyre_util.py
+++ b/tests/spyre_util.py
@@ -6,7 +6,7 @@
 import sys
 import time
 from pathlib import Path
-from typing import Optional
+from typing import NamedTuple, Optional
 
 import openai
 import pytest
@@ -55,6 +55,14 @@ def patch_warmup_shapes(warmup_shapes: DecodeWarmupShapes
                            ','.join(str(val) for val in warmup_new_tokens))
 
 
+class ModelInfo(NamedTuple):
+    name: str
+    revision: str | None = None
+
+    def __str__(self):
+        return f"ModelInfo({self.name}@{self.revision})"
+
+
 class RemoteOpenAIServer:
     """Subprocess wrapper that boots a vllm server with `vllm serve` for testing
     against"""
@@ -63,7 +71,7 @@ class RemoteOpenAIServer:
 
     def __init__(
         self,
-        model: str,
+        model: str | ModelInfo,
         vllm_serve_args: list[str],
         *,
         env_dict: Optional[dict[str, str]] = None,
@@ -74,6 +82,14 @@ def __init__(
         # NB: This implementation does not ensure that the model is downloaded
         # before booting the server, it should be used with models already
         # cached on disk
+        if isinstance(model, ModelInfo):
+            if model.revision is not None:
+                vllm_serve_args = vllm_serve_args + [
+                    "--revision", model.revision
+                ]
+            model_name = model.name
+        else:
+            model_name = model
 
         if auto_port:
             if "-p" in vllm_serve_args or "--port" in vllm_serve_args:
@@ -94,7 +110,7 @@ def __init__(
         parser = FlexibleArgumentParser(
             description="vLLM's remote OpenAI server.")
         parser = make_arg_parser(parser)
-        args = parser.parse_args(["--model", model, *vllm_serve_args])
+        args = parser.parse_args(["--model", model_name, *vllm_serve_args])
         self.host = str(args.host or "localhost")
         self.port = int(args.port)
 
@@ -102,7 +118,7 @@ def __init__(
         if env_dict is not None:
             env.update(env_dict)
         self.proc = subprocess.Popen(
-            ["vllm", "serve", model, *vllm_serve_args],
+            ["vllm", "serve", model_name, *vllm_serve_args],
             env=env,
             stdout=sys.stdout,
             stderr=sys.stderr,
@@ -204,6 +220,8 @@ def get_spyre_backend_list():
 # Multiple models can be specified with a comma separated list in
 # VLLM_SPYRE_TEST_MODEL_LIST
 def get_spyre_model_list(isEmbeddings=False, isScoring=False):
+    """Returns a list of pytest.params. The values are NamedTuples with a name
+    and revision field."""
     user_test_model_list = os.environ.get("VLLM_SPYRE_TEST_MODEL_LIST")
     if not user_test_model_list:
         return _default_test_models(isEmbeddings, isScoring)
@@ -221,31 +239,45 @@ def get_spyre_model_list(isEmbeddings=False, isScoring=False):
     for model in user_test_model_list.split(","):
         model_path = str(spyre_model_dir_path / model.strip())
         test_model_list.append(
-            pytest.param(model_path, marks=marks, id=model.strip()))
+            pytest.param(ModelInfo(name=model_path),
+                         marks=marks,
+                         id=model.strip()))
     return test_model_list
 
 
 def _default_test_models(isEmbeddings=False, isScoring=False):
     """Return the default set of test models as pytest parameterizations"""
     if isEmbeddings:
-        model = "sentence-transformers/all-roberta-large-v1"
-        return [pytest.param(model, marks=[pytest.mark.embedding], id=model)]
+        model = ModelInfo(name="sentence-transformers/all-roberta-large-v1",
+                          revision="cf74d8acd4f198de950bf004b262e6accfed5d2c")
+        return [
+            pytest.param(model, marks=[pytest.mark.embedding], id=model.name)
+        ]
 
     if isScoring:
-        model = "cross-encoder/stsb-roberta-large"
-        return [pytest.param(model, marks=[pytest.mark.scoring], id=model)]
+        model = ModelInfo(name="cross-encoder/stsb-roberta-large",
+                          revision="2b12c2c0088918e76151fd5937b7bba986ef1f98")
+        return [
+            pytest.param(model, marks=[pytest.mark.scoring], id=model.name)
+        ]
 
     # Decoders
     # We run tests for both the full-precision bf16 and fp8-quantized models,
     # but by default the `pytest.mark.quantized` marker is de-selected unless
     # the test command includes `-m quantized`.
-    tinygranite = "ibm-ai-platform/micro-g3.3-8b-instruct-1b"
-    tinygranite_fp8 = "ibm-ai-platform/micro-g3.3-8b-instruct-1b-FP8"
+    tinygranite = ModelInfo(
+        name="ibm-ai-platform/micro-g3.3-8b-instruct-1b",
+        revision="6e9c6465a9d7e5e9fa35004a29f0c90befa7d23f")
+    tinygranite_fp8 = ModelInfo(
+        name="ibm-ai-platform/micro-g3.3-8b-instruct-1b-FP8",
+        revision="0dff8bacb968836dbbc7c2895c6d9ead0a05dc9e")
     params = [
-        pytest.param(tinygranite, marks=[pytest.mark.decoder], id=tinygranite),
+        pytest.param(tinygranite,
+                     marks=[pytest.mark.decoder],
+                     id=tinygranite.name),
         pytest.param(tinygranite_fp8,
                      marks=[pytest.mark.decoder, pytest.mark.quantized],
-                     id=tinygranite_fp8)
+                     id=tinygranite_fp8.name)
     ]
     return params
 
diff --git a/tests/utils/test_spyre_model_list.py b/tests/utils/test_spyre_model_list.py
index dd57f89cc..502f902f6 100644
--- a/tests/utils/test_spyre_model_list.py
+++ b/tests/utils/test_spyre_model_list.py
@@ -13,13 +13,13 @@ def test_get_spyre_model_list(monkeypatch):
         m.setenv("VLLM_SPYRE_TEST_MODEL_LIST", "llama-194m, " \
                  "all-roberta-large-v1")
         model_list = get_spyre_model_list()
-        assert model_list[0].values[0] == "models/llama-194m"
-        assert model_list[1].values[0] == "models/all-roberta-large-v1"
+        assert model_list[0].values[0].name == "models/llama-194m"
+        assert model_list[1].values[0].name == "models/all-roberta-large-v1"
 
     with monkeypatch.context() as m:
         m.setenv("VLLM_SPYRE_TEST_MODEL_DIR", "")
         m.setenv("VLLM_SPYRE_TEST_MODEL_LIST", "llama-194m, " \
             "all-roberta-large-v1")
         model_list = get_spyre_model_list()
-        assert model_list[0].values[0] == "llama-194m"
-        assert model_list[1].values[0] == "all-roberta-large-v1"
+        assert model_list[0].values[0].name == "llama-194m"
+        assert model_list[1].values[0].name == "all-roberta-large-v1"
diff --git a/tests/utils/test_upstream_compatibility.py b/tests/utils/test_upstream_compatibility.py
index cf8d857fd..38f6509ca 100644
--- a/tests/utils/test_upstream_compatibility.py
+++ b/tests/utils/test_upstream_compatibility.py
@@ -2,6 +2,7 @@
 import os
 
 import pytest
+from spyre_util import ModelInfo
 
 from vllm_spyre.compat_utils import dataclass_fields
 
@@ -32,11 +33,12 @@ def test_vllm_bert_support():
 
 
 @pytest.mark.cpu
-def test_model_config_task(model: str):
+def test_model_config_task(model: ModelInfo):
 
     from vllm.engine.arg_utils import EngineArgs
 
-    vllm_config = EngineArgs(model=model).create_engine_config()
+    vllm_config = EngineArgs(model=model.name,
+                             revision=model.revision).create_engine_config()
     model_config = vllm_config.model_config
 
     task = getattr(model_config, "task", None)