diff --git a/README.md b/README.md
index 916c91479..27a467e81 100644
--- a/README.md
+++ b/README.md
@@ -183,6 +183,12 @@ Since Liger Kernel is 100% Triton-based, it works seamlessly with Torch Compile.
 
 [CONTRIBUTING GUIDE](https://github.com/linkedin/Liger-Kernel/blob/main/CONTRIBUTING.md)
 
+## Acknowledgement
+
+- [flash-attn](https://github.com/Dao-AILab/flash-attention) and [Unsloth](https://github.com/unslothai/unsloth) for inspiration in Triton kernels for training
+- [tiny shakespeare dataset](https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt) for convergence testing by andrej karpathy
+
+
 ## License
 
 [BSD 2-CLAUSE](https://github.com/linkedin/Liger-Kernel/blob/main/LICENSE)
diff --git a/test/convergence/test_mini_models_no_logits.py b/test/convergence/test_mini_models_no_logits.py
index f2b5f7f58..b36f655ad 100644
--- a/test/convergence/test_mini_models_no_logits.py
+++ b/test/convergence/test_mini_models_no_logits.py
@@ -26,6 +26,7 @@ class MiniModelConfig:
 
 MINI_MODEL_SETUPS = {
     "mini_llama3": MiniModelConfig(
+        # TODO (easy): replace with oss public path
         tokenizer_path="/shared/public/models/Meta-Llama-3-8B/",
         liger_kernel_patch_func=apply_liger_kernel_to_llama,
         model_class=LlamaForCausalLM,
diff --git a/test/transformers/test_cross_entropy.py b/test/transformers/test_cross_entropy.py
index 98f15abfd..71b81d7f0 100644
--- a/test/transformers/test_cross_entropy.py
+++ b/test/transformers/test_cross_entropy.py
@@ -155,16 +155,8 @@ def test_correctness_with_ignore_index(
 @pytest.mark.parametrize(
     "scalar, dtype, atol, rtol",
     [
-        # (0.01, torch.bfloat16, 1e-8, 5e-2),
-        # (0.1, torch.bfloat16, 1e-8, 5e-2),
         (1.0, torch.bfloat16, 1e-8, 5e-2),
-        # (10.0, torch.bfloat16, 1e-8, 5e-2),
-        # (100.0, torch.bfloat16, 1e-8, 5e-2),
-        # (0.01, torch.float32, 1e-8, 1e-6),
-        # (0.1, torch.float32, 1e-8, 1e-6),
         (1.0, torch.float32, 1e-8, 1e-6),
-        # (10.0, torch.float32, 1e-8, 1e-6),
-        # (100.0, torch.float32, 1e-8, 1e-6),
     ],
 )
 def test_correctness_not_last_layer(B, T, V, scalar, dtype, atol, rtol):
diff --git a/test/transformers/test_fused_linear_cross_entropy.py b/test/transformers/test_fused_linear_cross_entropy.py
index 13af97a9e..c21070340 100644
--- a/test/transformers/test_fused_linear_cross_entropy.py
+++ b/test/transformers/test_fused_linear_cross_entropy.py
@@ -58,6 +58,7 @@ def forward(self, x, y):
     [
         (2, 4, 512, 512),
         (8, 2048, 4096, 32000),  # llama2, mistral
+        # Comment out to speed up testing
         # (4, 2048, 4096, 128256),  # llama3 8B
         # (4, 1024, 8192, 128256),  # llama3 70B
         (4, 423, 8192, 32000),  # random shape
diff --git a/test/transformers/test_geglu.py b/test/transformers/test_geglu.py
index 5329f3ca7..b06fa04bf 100644
--- a/test/transformers/test_geglu.py
+++ b/test/transformers/test_geglu.py
@@ -12,8 +12,6 @@
 )
 SLEEP_SECONDS = 0.1
 
-# TODO (yun dai): triton 3.0.0 breaks geglu due to tanh module issue
-
 
 @pytest.mark.parametrize(
     "bsz, seq_len, hidden_size, intermediate_size",
diff --git a/test/transformers/test_rms_norm.py b/test/transformers/test_rms_norm.py
index 0ede8c440..1d0a44905 100644
--- a/test/transformers/test_rms_norm.py
+++ b/test/transformers/test_rms_norm.py
@@ -72,5 +72,5 @@ def test_correctness(bs, sl, hd, dtype, atol, rtol):
         )
         is True
     )
-    # import pdb; pdb.set_trace()
+
     assert torch.allclose(h1.grad, h2.grad, atol=atol, rtol=rtol) is True
diff --git a/test/transformers/test_swiglu.py b/test/transformers/test_swiglu.py
index c9e0a6afe..14132c2a9 100644
--- a/test/transformers/test_swiglu.py
+++ b/test/transformers/test_swiglu.py
@@ -29,7 +29,7 @@
         # atol is for small values: they have more difference, so set atol higher
         # rtol is for larger values: they are very close, so set rtol lower
         (torch.float32, 1e-0, 1e-5),
-        # TODO: we should find a better way to tune this lol. 1e4 is too large apparently
+        # TODO: we should find a better way to tune this. 1e4 is too large apparently
         (torch.bfloat16, 1e4, 1e-2),
     ],
 )