Instructions to use EdgarDesnos/MNLP_M2_quantized_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EdgarDesnos/MNLP_M2_quantized_model with Transformers:

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

pipe = pipeline("text-generation", model="EdgarDesnos/MNLP_M2_quantized_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EdgarDesnos/MNLP_M2_quantized_model")
model = AutoModelForCausalLM.from_pretrained("EdgarDesnos/MNLP_M2_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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EdgarDesnos/MNLP_M2_quantized_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EdgarDesnos/MNLP_M2_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": "EdgarDesnos/MNLP_M2_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EdgarDesnos/MNLP_M2_quantized_model

SGLang

How to use EdgarDesnos/MNLP_M2_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 "EdgarDesnos/MNLP_M2_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": "EdgarDesnos/MNLP_M2_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 "EdgarDesnos/MNLP_M2_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": "EdgarDesnos/MNLP_M2_quantized_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EdgarDesnos/MNLP_M2_quantized_model with Docker Model Runner:
```
docker model run hf.co/EdgarDesnos/MNLP_M2_quantized_model
```

MNLP_M2_quantized_model / sciq-ft /checkpoint-8000 /tokenizer.json

EdgarDesnos

Training in progress, step 162

b022a3f verified over 1 year ago

History Blame Contribute Delete

11.4 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 9cafde5dbb2297befc366693298d8e8fb172b6c5c4acd5cac13516a063c104ea
Size of remote file:: 11.4 MB
SHA256:: 34931275ec59c5cdd5d27556564a3b1b9afb4a0706c8b785f0ee26b9c11b05e7

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.