Instructions to use SciPhi/SciPhi-Mistral-7B-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SciPhi/SciPhi-Mistral-7B-32k with Transformers:

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

pipe = pipeline("text-generation", model="SciPhi/SciPhi-Mistral-7B-32k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SciPhi/SciPhi-Mistral-7B-32k")
model = AutoModelForCausalLM.from_pretrained("SciPhi/SciPhi-Mistral-7B-32k")

Inference
Local Apps Settings

vLLM

How to use SciPhi/SciPhi-Mistral-7B-32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SciPhi/SciPhi-Mistral-7B-32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SciPhi/SciPhi-Mistral-7B-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SciPhi/SciPhi-Mistral-7B-32k

SGLang

How to use SciPhi/SciPhi-Mistral-7B-32k 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 "SciPhi/SciPhi-Mistral-7B-32k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SciPhi/SciPhi-Mistral-7B-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "SciPhi/SciPhi-Mistral-7B-32k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SciPhi/SciPhi-Mistral-7B-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SciPhi/SciPhi-Mistral-7B-32k with Docker Model Runner:
```
docker model run hf.co/SciPhi/SciPhi-Mistral-7B-32k
```

SciPhi-Mistral-7B-32k / model-00003-of-00003.safetensors

SFconvertbot

Adding `safetensors` variant of this model

d146650 verified over 1 year ago

Download

History Blame

9.08 GB

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:: 85d7fb292084cedf329cb212d2a07e7923df34dbbd197f5a4d904f31531e5a0e
Size of remote file:: 9.08 GB
SHA256:: 4d3c90292cf0377bacc6b5ba5eabf940a3a46de22597327ea96553aaf08e4f6a

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