Instructions to use rpchinhara/25M2573-Week05-Compression20-Submission01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rpchinhara/25M2573-Week05-Compression20-Submission01 with Transformers:

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

pipe = pipeline("text-generation", model="rpchinhara/25M2573-Week05-Compression20-Submission01")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("rpchinhara/25M2573-Week05-Compression20-Submission01")
model = AutoModelForMultimodalLM.from_pretrained("rpchinhara/25M2573-Week05-Compression20-Submission01", device_map="auto")
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 Settings

vLLM

How to use rpchinhara/25M2573-Week05-Compression20-Submission01 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rpchinhara/25M2573-Week05-Compression20-Submission01"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rpchinhara/25M2573-Week05-Compression20-Submission01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rpchinhara/25M2573-Week05-Compression20-Submission01

SGLang

How to use rpchinhara/25M2573-Week05-Compression20-Submission01 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 "rpchinhara/25M2573-Week05-Compression20-Submission01" \
    --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": "rpchinhara/25M2573-Week05-Compression20-Submission01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "rpchinhara/25M2573-Week05-Compression20-Submission01" \
        --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": "rpchinhara/25M2573-Week05-Compression20-Submission01",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rpchinhara/25M2573-Week05-Compression20-Submission01 with Docker Model Runner:
```
docker model run hf.co/rpchinhara/25M2573-Week05-Compression20-Submission01
```

Qwen3.5-4B Model Compression

Model Description

This repository contains a compressed version of Qwen3.5-4B, developed as part of the CS6013: Efficient AI course project. The objective is to reduce model memory while preserving performance on mathematical reasoning tasks using model compression techniques.

Base model: Qwen/Qwen3.5-4B
Developed by: Rudrapratap Chinhara
Course: CS6013 – Efficient AI
Affliation: IIT Bombay
Language: English
License: CC BY-NC 4.0

Citation

If you use this repository, please cite the original Qwen model:

@misc{qwen3.5,
    title  = {{Qwen3.5}: Towards Native Multimodal Agents},
    author = {{Qwen Team}},
    month  = {February},
    year   = {2026},
    url    = {https://qwen.ai/blog?id=qwen3.5}
}

Acknowledgements

This work is based on the Qwen3.5-4B model released by the Qwen Team and was developed for the CS6013 Efficient AI course project.

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Safetensors

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Tensor type

I32

BF16

F16

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