Instructions to use zay25/MNLP_M3_quantized_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zay25/MNLP_M3_quantized_model with Transformers:

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

pipe = pipeline("text-generation", model="zay25/MNLP_M3_quantized_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zay25/MNLP_M3_quantized_model")
model = AutoModelForCausalLM.from_pretrained("zay25/MNLP_M3_quantized_model", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zay25/MNLP_M3_quantized_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zay25/MNLP_M3_quantized_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zay25/MNLP_M3_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zay25/MNLP_M3_quantized_model

SGLang

How to use zay25/MNLP_M3_quantized_model 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 "zay25/MNLP_M3_quantized_model" \
    --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": "zay25/MNLP_M3_quantized_model",
		"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 "zay25/MNLP_M3_quantized_model" \
        --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": "zay25/MNLP_M3_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zay25/MNLP_M3_quantized_model with Docker Model Runner:
```
docker model run hf.co/zay25/MNLP_M3_quantized_model
```

MNLP_M3_quantized_model / README.md

zay25

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	---
	library_name: transformers
	tags: []
	---
	# Model Card for `zay25/MNLP_M3_quantized_model`

	This model is a quantized version of a multiple-choice question answering (MCQA) model fine-tuned on STEM datasets. It uses Activation-aware Weight Quantization (AWQ) to reduce model size and VRAM usage while preserving strong performance. The model is well-suited for memory- and latency-constrained environments.

	---

	## Model Details

	- Developed by: Zeineb Mellouli (EPFL, CS-552 Project)
	- Base model: `hssawhney/Best-Performing-Model` (Qwen3-0.6B-Base)
	- Quantization: AWQ (4-bit weights, 16-bit activations)
	- Architecture: Transformer-based Causal Language Model
	- Language: English
	- License: Apache 2.0

	---

	## Uses

	### Direct Use

	This model is intended for multiple-choice question answering (MCQA) tasks, particularly in science, math, and engineering education datasets. It is optimized for inference on GPUs with limited VRAM (e.g., A10, T4, or laptop GPUs).

	### Out-of-Scope Use

	- Not intended for open-ended or dialog generation
	- Not suitable for high-stakes decision-making or critical applications without human oversight


	## Training Details

	- Quantization method: Post-training quantization using [AWQ (Activation-aware Weight Quantization)](https://github.com/mit-han-lab/awq) via the `awq` library
	- Base model: `hssawhney/Best-Performing-Model`, fine-tuned on MCQA-style reasoning tasks
	- Quantization configuration:
	- 4-bit weights (`w_bit = 4`)
	- Group size: 64
	- Per-channel zero point: enabled
	- Calibration dataset: 512 samples from `hssawhney/Reasoning-Dataset`

	---

	## How to Use

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("zay25/MNLP_M3_quantized_model", trust_remote_code=True)
	tokenizer = AutoTokenizer.from_pretrained("zay25/MNLP_M3_quantized_model")