Instructions to use DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8")
model = AutoModelForCausalLM.from_pretrained("DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8")
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

vLLM

How to use DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8

SGLang

How to use DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8 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 "DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8" \
    --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": "DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8",
		"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 "DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8" \
        --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": "DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8 with Docker Model Runner:
```
docker model run hf.co/DataSnake/Mistral-Nemo-Instruct-2407-NVFP4-FP8
```

DataSnake commited on Apr 8

Commit

0e918cc

verified ·

1 Parent(s): b17b8fb

Upload perplexity-all.png

Browse files

Files changed (2) hide show

.gitattributes +1 -0
perplexity-all.png +3 -0

.gitattributes CHANGED Viewed

@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 perplexity-plot.png filter=lfs diff=lfs merge=lfs -text

 *tfevents* filter=lfs diff=lfs merge=lfs -text
 tokenizer.json filter=lfs diff=lfs merge=lfs -text
 perplexity-plot.png filter=lfs diff=lfs merge=lfs -text
+perplexity-all.png filter=lfs diff=lfs merge=lfs -text

perplexity-all.png ADDED Viewed

Git LFS Details

SHA256: 4296625563cbe02db8886784b30bc86129a58e72bc45079dadbeff2ccf0d419d
Pointer size: 131 Bytes
Size of remote file: 141 kB