Instructions to use prithivMLmods/Chamaeleontis-7B-Reason-rVL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Chamaeleontis-7B-Reason-rVL with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/Chamaeleontis-7B-Reason-rVL")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("prithivMLmods/Chamaeleontis-7B-Reason-rVL")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Chamaeleontis-7B-Reason-rVL")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Chamaeleontis-7B-Reason-rVL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Chamaeleontis-7B-Reason-rVL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Chamaeleontis-7B-Reason-rVL",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Chamaeleontis-7B-Reason-rVL

SGLang

How to use prithivMLmods/Chamaeleontis-7B-Reason-rVL with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Chamaeleontis-7B-Reason-rVL" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Chamaeleontis-7B-Reason-rVL",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Chamaeleontis-7B-Reason-rVL" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Chamaeleontis-7B-Reason-rVL",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Chamaeleontis-7B-Reason-rVL with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Chamaeleontis-7B-Reason-rVL
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Chamaeleontis-7B-Reason-rVL

The Chamaeleontis-7B-Reason-rVL model is a general-purpose multimodal reasoning model built on Qwen2.5-VL-7B-Instruct, optimized for image and video frame understanding, contextual analysis, and natural language reasoning. It excels at extracting structured insights from both static visuals and video streams through step-by-step logical interpretation and visual common sense grounding.

Chamaeleontis: Reason-based Qwen2.5VL model for visual understanding and CoT reasoning

Key Capabilities

Visual Frame Reasoning: Understands and interprets key frames from videos or images with attention to spatial and physical relationships.
Chain-of-Thought Natural Language Output: Generates thoughtful, logically connected answers for visual queries.
General-Purpose Multimodal Insight: Applicable across diverse domains for both still image and sequential video analysis.
Custom Frame Selection Logic: Applies smart frame sampling for high-relevance visual inputs during training and inference.
Common Sense Visual Comprehension: Supports physical reasoning, object interaction detection, and real-world logic estimation from visuals.
Q&A Over Visual Inputs: Ideal for use cases like question answering, summarization, and temporal information extraction from visual sources.

Quick Start with Transformers

from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/Chamaeleontis-7B-Reason-rVL", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Chamaeleontis-7B-Reason-rVL")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "video", "video": "path/to/your/video.mp4"},
            {"type": "text", "text": "Briefly describe the physical events and interactions in this video using logical steps."},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)

inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Use Cases

Image and Video Q&A: Responds to natural language queries with logical, visual-grounded answers.
Physical Event Interpretation: Understands object motion, cause-effect dynamics, and temporal interactions.
Visual Insight Summarization: Extracts core narrative and insights from multimedia content.
Educational Content Understanding: Supports reasoning-based tasks in educational and research video analysis.
Multimodal Reasoning: Merges visual and textual understanding to support complex interpretation tasks.

Limitations

Experimental Architecture: While robust, it may need tuning for domain-specific datasets.
Inference Overhead: High GPU memory requirements for long or high-resolution video processing.
Frame Sampling Bias: Quality depends on relevance and spacing of sampled frames.
Context Window Boundaries: Very long inputs may require segmenting or hierarchical reasoning strategies.