Instructions to use drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4")
model = AutoModelForCausalLM.from_pretrained("drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4")
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 drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4

SGLang

How to use drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4 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 "drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4" \
    --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": "drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4",
		"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 "drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4" \
        --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": "drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4 with Docker Model Runner:
```
docker model run hf.co/drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4
```

Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4

INT4 weight-only quantization of mistralai/Mistral-7B-Instruct-v0.3.

Mistral-7B-Instruct-v0.3 in NVFP4 W4A4 (4-bit weights + 4-bit activations). Native vLLM compressed-tensors. Hopper/Ada/Blackwell tensor cores supported. About 4 GB on disk.

Property	Value
Base model	mistralai/Mistral-7B-Instruct-v0.3
Quantization	INT4 weight-only
Approx. on-disk size	~4.5 GB
License	Apache License, Version 2.0
Languages	English

Load (vLLM)

vllm serve drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4 \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.94

from vllm import LLM, SamplingParams
llm = LLM(model="drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4", max_model_len=32768)
print(llm.generate(["Hello!"], SamplingParams(max_tokens=128))[0].outputs[0].text)

Footprint

~4.5 GB on disk. Recommended VRAM: enough headroom for KV cache.

License & attribution

This artifact is a derivative work of mistralai/Mistral-7B-Instruct-v0.3, released by its original authors under the Apache License, Version 2.0.

This artifact is distributed under the same license. The full license text is included in LICENSE, and required attribution is in NOTICE.

License text: https://www.apache.org/licenses/LICENSE-2.0 Source model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3

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Safetensors

Model size

4B params

Tensor type

F32

BF16

F8_E4M3

Model tree for drawais/Mistral-7B-Instruct-v0.3-SpinQuant-NVFP4

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Quantized

(247)

this model