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
```

Mistral-Nemo-Instruct-2407-NVFP4-FP8 / benchmarks.md

DataSnake

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	\|\|\|Mistral-Nemo-Instruct-2407-NVFP4\|\|Mistral-Nemo-Instruct-2407-NVFP4-4over6\|\|Mistral-Nemo-Instruct-2407-NVFP4-FP8-RTN\|\|Mistral-Nemo-Instruct-2407-NVFP4-FP8\|\|
	\|-\|-\|-\|-\|-\|-\|-\|-\|-\|-\|
	\|Task\|Metric\|Value\|Stderr\|Value\|Stderr\|Value\|Stderr\|Value\|Stderr\|
	\|coqa\|em\|0.5392\|0.0199\|0.5498\|0.0202\|0.5683\|0.0196\|0.5733\|0.0195\|
	\|\|f1\|0.7182\|0.0151\|0.7212\|0.0154\|0.7401\|0.0142\|0.7347\|0.0150\|
	\|hellaswag\|acc\|0.6186\|0.0048\|0.6194\|0.0048\|0.6238\|0.0048\|0.6240\|0.0048\|
	\|\|acc_norm\|0.8084\|0.0039\|0.8132\|0.0039\|0.8140\|0.0039\|0.8125\|0.0039\|
	\|ifeval\|inst_level_loose_acc\|0.5456\|N/A\|0.5683\|N/A\|0.5564\|N/A\|0.5767\|N/A\|
	\|\|inst_level_strict_acc\|0.4712\|N/A\|0.5096\|N/A\|0.5012\|N/A\|0.5108\|N/A\|
	\|\|prompt_level_loose_acc\|0.4603\|0.0214\|0.4824\|0.0215\|0.4621\|0.0215\|0.4917\|0.0215\|
	\|\|prompt_level_strict_acc\|0.3808\|0.0209\|0.4122\|0.0212\|0.3993\|0.0211\|0.4196\|0.0212\|
	\|lambada_openai\|acc\|0.7584\|0.0060\|0.7687\|0.0059\|0.7619\|0.0059\|0.7726\|0.0058\|
	\|\|perplexity\|3.0229\|0.0563\|2.9546\|0.0541\|2.9591\|0.0556\|2.9233\|0.0542\|
	\|lambada_openai_cloze\|acc\|0.3122\|0.0065\|0.2983\|0.0064\|0.3315\|0.0066\|0.3317\|0.0066\|
	\|\|perplexity\|29.8427\|0.7625\|30.0355\|0.7780\|26.6970\|0.6838\|26.6948\|0.6858\|
	\|lambada_standard\|acc\|0.6885\|0.0065\|0.6907\|0.0064\|0.6971\|0.0064\|0.6926\|0.0064\|
	\|\|perplexity\|3.6401\|0.0766\|3.6600\|0.0756\|3.4930\|0.0721\|3.5514\|0.0734\|
	\|lambada_standard_cloze\|acc\|0.2259\|0.0058\|0.2467\|0.0060\|0.2583\|0.0061\|0.2837\|0.0063\|
	\|\|perplexity\|44.8440\|1.1469\|40.9925\|1.0271\|37.4110\|0.9371\|35.5615\|0.8741\|
	\|commonsense_qa\|acc\|0.5774\|0.0141\|0.5921\|0.0141\|0.6061\|0.0140\|0.6208\|0.0139\|
	\|mmlu\|acc\|0.6325\|0.0038\|0.6364\|0.0038\|0.6434\|0.0038\|0.6454\|0.0038\|
	\|\|acc\|0.5673\|0.0067\|0.5779\|0.0067\|0.5819\|0.0067\|0.5864\|0.0067\|
	\|\|acc\|0.7123\|0.0078\|0.7110\|0.0078\|0.7210\|0.0078\|0.7277\|0.0077\|
	\|\|acc\|0.7491\|0.0076\|0.7504\|0.0076\|0.7563\|0.0076\|0.7563\|0.0076\|
	\|\|acc\|0.5373\|0.0085\|0.5392\|0.0085\|0.5487\|0.0084\|0.5442\|0.0085\|
	\|openbookqa\|acc\|0.3680\|0.0216\|0.3920\|0.0219\|0.4040\|0.0220\|0.4040\|0.0220\|
	\|\|acc_norm\|0.4700\|0.0223\|0.4720\|0.0223\|0.4780\|0.0224\|0.4880\|0.0224\|
	\|winogrande\|acc\|0.7672\|0.0119\|0.7553\|0.0121\|0.7514\|0.0121\|0.7545\|0.0121\|
	\|triviaqa\|exact_match\|0.5953\|0.0037\|0.6011\|0.0037\|0.6105\|0.0036\|0.6184\|0.0036\|
	\|truthfulqa_mc1\|acc\|0.3782\|0.0170\|0.3831\|0.0170\|0.3892\|0.0171\|0.3917\|0.0171\|
	\|truthfulqa_mc2\|acc\|0.5284\|0.0150\|0.5367\|0.0149\|0.5390\|0.0151\|0.5475\|0.0150\|

	\|\|\|Mistral-Nemo-Instruct-2407-NVFP4\|\|Mistral-Nemo-Instruct-2407-NVFP4-4over6\|\|Mistral-Nemo-Instruct-2407-NVFP4-FP8-RTN\|\|Mistral-Nemo-Instruct-2407-NVFP4-FP8\|\|
	\|-\|-\|-\|-\|-\|-\|-\|-\|-\|-\|
	\|Task\|Metric\|Value\|Stderr\|Value\|Stderr\|Value\|Stderr\|Value\|Stderr\|
	\|coqa\|em\|0.5392\|0.0199\|0.5498\|0.0202\|0.5683\|0.0196\|0.5733\|0.0195\|
	\|\|f1\|0.7182\|0.0151\|0.7212\|0.0154\|0.7401\|0.0142\|0.7347\|0.0150\|
	\|hellaswag\|acc\|0.6186\|0.0048\|0.6194\|0.0048\|0.6238\|0.0048\|0.6240\|0.0048\|
	\|\|acc_norm\|0.8084\|0.0039\|0.8132\|0.0039\|0.8140\|0.0039\|0.8125\|0.0039\|
	\|ifeval\|inst_level_loose_acc\|0.5456\|N/A\|0.5683\|N/A\|0.5564\|N/A\|0.5767\|N/A\|
	\|\|inst_level_strict_acc\|0.4712\|N/A\|0.5096\|N/A\|0.5012\|N/A\|0.5108\|N/A\|
	\|\|prompt_level_loose_acc\|0.4603\|0.0214\|0.4824\|0.0215\|0.4621\|0.0215\|0.4917\|0.0215\|
	\|\|prompt_level_strict_acc\|0.3808\|0.0209\|0.4122\|0.0212\|0.3993\|0.0211\|0.4196\|0.0212\|
	\|lambada_openai\|acc\|0.7584\|0.0060\|0.7687\|0.0059\|0.7619\|0.0059\|0.7726\|0.0058\|
	\|\|perplexity\|3.0229\|0.0563\|2.9546\|0.0541\|2.9591\|0.0556\|2.9233\|0.0542\|
	\|lambada_openai_cloze\|acc\|0.3122\|0.0065\|0.2983\|0.0064\|0.3315\|0.0066\|0.3317\|0.0066\|
	\|\|perplexity\|29.8427\|0.7625\|30.0355\|0.7780\|26.6970\|0.6838\|26.6948\|0.6858\|
	\|lambada_standard\|acc\|0.6885\|0.0065\|0.6907\|0.0064\|0.6971\|0.0064\|0.6926\|0.0064\|
	\|\|perplexity\|3.6401\|0.0766\|3.6600\|0.0756\|3.4930\|0.0721\|3.5514\|0.0734\|
	\|lambada_standard_cloze\|acc\|0.2259\|0.0058\|0.2467\|0.0060\|0.2583\|0.0061\|0.2837\|0.0063\|
	\|\|perplexity\|44.8440\|1.1469\|40.9925\|1.0271\|37.4110\|0.9371\|35.5615\|0.8741\|
	\|commonsense_qa\|acc\|0.5774\|0.0141\|0.5921\|0.0141\|0.6061\|0.0140\|0.6208\|0.0139\|
	\|mmlu\|acc\|0.6325\|0.0038\|0.6364\|0.0038\|0.6434\|0.0038\|0.6454\|0.0038\|
	\|\|acc\|0.5673\|0.0067\|0.5779\|0.0067\|0.5819\|0.0067\|0.5864\|0.0067\|
	\|\|acc\|0.7123\|0.0078\|0.7110\|0.0078\|0.7210\|0.0078\|0.7277\|0.0077\|
	\|\|acc\|0.7491\|0.0076\|0.7504\|0.0076\|0.7563\|0.0076\|0.7563\|0.0076\|
	\|\|acc\|0.5373\|0.0085\|0.5392\|0.0085\|0.5487\|0.0084\|0.5442\|0.0085\|
	\|openbookqa\|acc\|0.3680\|0.0216\|0.3920\|0.0219\|0.4040\|0.0220\|0.4040\|0.0220\|
	\|\|acc_norm\|0.4700\|0.0223\|0.4720\|0.0223\|0.4780\|0.0224\|0.4880\|0.0224\|
	\|winogrande\|acc\|0.7672\|0.0119\|0.7553\|0.0121\|0.7514\|0.0121\|0.7545\|0.0121\|
	\|triviaqa\|exact_match\|0.5953\|0.0037\|0.6011\|0.0037\|0.6105\|0.0036\|0.6184\|0.0036\|
	\|truthfulqa_mc1\|acc\|0.3782\|0.0170\|0.3831\|0.0170\|0.3892\|0.0171\|0.3917\|0.0171\|
	\|truthfulqa_mc2\|acc\|0.5284\|0.0150\|0.5367\|0.0149\|0.5390\|0.0151\|0.5475\|0.0150\|