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Pluggable model adapters for run_custom_model.py (VLMEvalKit-free).
An adapter is any class with:
class MyModel:
def __init__(self, model_name="...", **kwargs): ...
def generate(self, text: str, images: list[PIL.Image.Image]) -> str: ...
`images` is a list of PIL images already sampled by the runner (16 video frames,
or a single image, or frames + an observation image for interleaved tasks).
Return the model's raw text answer; eval.py then grades it.
Select an adapter on the CLI: --model_impl bear_models:CosmosReason1
"""
class EchoModel:
"""Dependency-free adapter for smoke-testing the pipeline (no real model)."""
def __init__(self, model_name="echo", **kwargs):
self.model_name = model_name
def generate(self, text, images):
return f"[echo] got {len(images)} image(s). I choose option A."
class CosmosReason1:
"""
NVIDIA Cosmos-Reason1-7B (built on Qwen2.5-VL-7B), via Hugging Face transformers.
pip install "transformers>=4.51" accelerate torchvision
Notes:
- Recommend max_new_tokens=4096 so chain-of-thought isn't truncated.
- The runner passes already-sampled video frames as a list of images.
"""
def __init__(self, model_name="nvidia/Cosmos-Reason1-7B", max_new_tokens=4096, device_map="auto", **kwargs):
import torch
from transformers import AutoProcessor, AutoModelForMultimodalLM
self.model_name = model_name
self.max_new_tokens = max_new_tokens
self.processor = AutoProcessor.from_pretrained(model_name)
self.model = AutoModelForMultimodalLM.from_pretrained(
model_name, torch_dtype=torch.bfloat16, device_map=device_map
)
def generate(self, text, images):
content = [{"type": "image", "image": im} for im in images]
content.append({"type": "text", "text": text})
messages = [{"role": "user", "content": content}]
inputs = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(self.model.device)
out = self.model.generate(**inputs, max_new_tokens=self.max_new_tokens)
gen = out[0][inputs["input_ids"].shape[-1]:]
return self.processor.decode(gen, skip_special_tokens=True).strip()
class QwenVL:
"""
Generic adapter for Qwen2.5-VL-style image-text-to-text models
(e.g. Qwen/Qwen2.5-VL-7B-Instruct). Copy & tweak for your own HF model.
pip install "transformers>=4.49" accelerate torchvision
"""
def __init__(self, model_name="Qwen/Qwen2.5-VL-7B-Instruct", max_new_tokens=1024, device_map="auto", **kwargs):
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
self.model_name = model_name
self.max_new_tokens = max_new_tokens
self.processor = AutoProcessor.from_pretrained(model_name)
self.model = AutoModelForImageTextToText.from_pretrained(
model_name, torch_dtype=torch.bfloat16, device_map=device_map
)
def generate(self, text, images):
content = [{"type": "image", "image": im} for im in images]
content.append({"type": "text", "text": text})
messages = [{"role": "user", "content": content}]
inputs = self.processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt",
).to(self.model.device)
out = self.model.generate(**inputs, max_new_tokens=self.max_new_tokens)
gen = out[0][inputs["input_ids"].shape[-1]:]
return self.processor.decode(gen, skip_special_tokens=True).strip()
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