[Model] Add LFM2-VL model support

docs/models/supported_models.md

@@ -699,6 +699,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `KeyeVL1_5ForConditionalGeneration` | Keye-VL-1_5-8B | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-1_5-8B` | ✅︎ | ✅︎ |
 | `KimiVLForConditionalGeneration` | Kimi-VL-A3B-Instruct, Kimi-VL-A3B-Thinking | T + I<sup>+</sup> | `moonshotai/Kimi-VL-A3B-Instruct`, `moonshotai/Kimi-VL-A3B-Thinking` | | ✅︎ |
 | `LightOnOCRForConditionalGeneration`  | LightOnOCR-1B  | T + I<sup>+</sup> | `lightonai/LightOnOCR-1B`, etc | ✅︎ | ✅︎ |
+| `Lfm2VlForConditionalGeneration` | LFM2-VL | T + I<sup>+</sup> | `LiquidAI/LFM2-VL-450M`, `LiquidAI/LFM2-VL-3B`, `LiquidAI/LFM2-VL-8B-A1B`, etc. | ✅︎ | ✅︎ |
 | `Llama4ForConditionalGeneration` | Llama 4 | T + I<sup>+</sup> | `meta-llama/Llama-4-Scout-17B-16E-Instruct`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct`, etc. | ✅︎ | ✅︎ |
 | `Llama_Nemotron_Nano_VL` | Llama Nemotron Nano VL | T + I<sup>E+</sup> | `nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1` | ✅︎ | ✅︎ |
 | `LlavaForConditionalGeneration` | LLaVA-1.5, Pixtral (HF Transformers) | T + I<sup>E+</sup> | `llava-hf/llava-1.5-7b-hf`, `TIGER-Lab/Mantis-8B-siglip-llama3` (see note), `mistral-community/pixtral-12b`, etc. | ✅︎ | ✅︎ |

examples/offline_inference/vision_language.py

@@ -15,7 +15,7 @@
 from typing import NamedTuple
 
 from huggingface_hub import snapshot_download
-from transformers import AutoTokenizer
+from transformers import AutoProcessor, AutoTokenizer
 
 from vllm import LLM, EngineArgs, SamplingParams
 from vllm.assets.image import ImageAsset
@@ -875,6 +875,37 @@ def run_lightonocr(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+def run_lfm2_vl(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+
+    model_name = "LiquidAI/LFM2-VL-450M"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=4096,
+        limit_mm_per_prompt={modality: 1},
+    )
+
+    processor = AutoProcessor.from_pretrained(model_name)
+    messages = [
+        [
+            {
+                "role": "user",
+                "content": [{"type": "image"}, {"type": "text", "text": question}],
+            }
+        ]
+        for question in questions
+    ]
+    prompts = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 def run_llama4(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
 
@@ -1849,6 +1880,7 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
     "keye_vl1_5": run_keye_vl1_5,
     "kimi_vl": run_kimi_vl,
     "lightonocr": run_lightonocr,
+    "lfm2_vl": run_lfm2_vl,
     "llama4": run_llama4,
     "llava": run_llava,
     "llava-next": run_llava_next,

tests/models/registry.py

@@ -732,6 +732,10 @@ def check_available_online(
     "LightOnOCRForConditionalGeneration": _HfExamplesInfo(
         "lightonai/LightOnOCR-1B-1025"
     ),
+    "Lfm2VlForConditionalGeneration": _HfExamplesInfo(
+        "LiquidAI/LFM2-VL-450M",
+        min_transformers_version="5.0.0",
+    ),
     "Llama4ForConditionalGeneration": _HfExamplesInfo(
         "meta-llama/Llama-4-Scout-17B-16E-Instruct",
         max_model_len=10240,