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")
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
```

zay25 commited on Jun 9, 2025

Commit

4d62aa7

verified ·

1 Parent(s): d2b2a1e

update

Browse files

Files changed (1) hide show

README.md +9 -9

README.md CHANGED Viewed

@@ -30,15 +30,6 @@ This model is intended for multiple-choice question answering (MCQA) tasks, part
 - Not intended for open-ended or dialog generation
 - Not suitable for high-stakes decision-making or critical applications without human oversight
----
-## 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")
 ## Training Details
@@ -50,3 +41,12 @@ tokenizer = AutoTokenizer.from_pretrained("zay25/MNLP_M3_quantized_model")
   - Per-channel zero point: enabled
 - **Calibration dataset**: 512 samples from `hssawhney/Reasoning-Dataset`

 - Not intended for open-ended or dialog generation
 - Not suitable for high-stakes decision-making or critical applications without human oversight
 ## Training Details
   - 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")