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

Libraries

How to use Kikinoking/MNLP_M3_quantized_model with Transformers:

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

pipe = pipeline("text-generation", model="Kikinoking/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("Kikinoking/MNLP_M3_quantized_model")
model = AutoModelForCausalLM.from_pretrained("Kikinoking/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 Kikinoking/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 "Kikinoking/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": "Kikinoking/MNLP_M3_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kikinoking/MNLP_M3_quantized_model

SGLang

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

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

MNLP_M3_quantized_model / README.md

Kikinoking

Update README.md

45f7328 verified about 1 year ago

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	---
	library_name: transformers
	tags:
	- quantization
	- bitsandbytes
	- 4-bit
	- nf4
	- double-quant
	- mcqa
	---
	# Model Card for `Kikinoking/MNLP_M3_quantized_model`

	A 4-bit double-quantized (NF4 + nested quant) version of the MNLP_M3_mcqa_model, compressed with bitsandbytes. This model answers multiple-choice questions (MCQA) with minimal GPU memory usage.

	## Model Details

	- Model ID: `Kikinoking/MNLP_M3_quantized_model`
	- Quantization: 4-bit NF4 + nested quantization (`bnb_4bit_use_double_quant=True`)
	- Base model: `aidasvenc/MNLP_M3_mcqa_model`
	- Library: Transformers + bitsandbytes
	- Task: Multiple-choice question answering (MCQA)

	## Usage

	Load and run inference in just a few lines:

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_id = "Kikinoking/MNLP_M3_quantized_model"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="auto",
	torch_dtype="auto",
	low_cpu_mem_usage=True
	).eval()

	prompt = "What is the capital of France ?\nA) Lyon B) Marseille C) Paris D) Toulouse\nAnswer: "
	inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

	with torch.inference_mode():
	output = model.generate(**inputs, max_new_tokens=1)

	print("Answer:", tokenizer.decode(output[0], skip_special_tokens=True))

	##How It Was Built

	from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
	import torch

	base_id = "aidasvenc/MNLP_M3_mcqa_model"
	qcfg = BitsAndBytesConfig(
	load_in_4bit=True,
	bnb_4bit_quant_type="nf4",
	bnb_4bit_compute_dtype=torch.bfloat16,
	bnb_4bit_use_double_quant=True
	)

	tokenizer = AutoTokenizer.from_pretrained(base_id)
	model = AutoModelForCausalLM.from_pretrained(
	base_id,
	quantization_config=qcfg,
	device_map="auto",
	torch_dtype="auto"
	)

	# Push to Hugging Face Hub
	model.push_to_hub("Kikinoking/MNLP_M3_quantized_model", private=True)
	tokenizer.push_to_hub("Kikinoking/MNLP_M3_quantized_model")

	print("VRAM used (MiB):", torch.cuda.memory_reserved()/1024**2)