Instructions to use mattshumer/mistral-8x7b-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mattshumer/mistral-8x7b-chat with Transformers:

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

pipe = pipeline("text-generation", model="mattshumer/mistral-8x7b-chat", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("mattshumer/mistral-8x7b-chat", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mattshumer/mistral-8x7b-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mattshumer/mistral-8x7b-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mattshumer/mistral-8x7b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mattshumer/mistral-8x7b-chat

SGLang

How to use mattshumer/mistral-8x7b-chat 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 "mattshumer/mistral-8x7b-chat" \
    --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": "mattshumer/mistral-8x7b-chat",
		"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 "mattshumer/mistral-8x7b-chat" \
        --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": "mattshumer/mistral-8x7b-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mattshumer/mistral-8x7b-chat with Docker Model Runner:
```
docker model run hf.co/mattshumer/mistral-8x7b-chat
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

A very capable chat model built on top of the new Mistral MoE model, trained on the SlimOrca dataset for 1 epoch, using QLoRA.

Inference:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("mattshumer/mistral-8x7b-chat", low_cpu_mem_usage=True, device_map="auto", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("mattshumer/mistral-8x7b-chat")
x = tok.encode(PROMPT_GOES_HERE, return_tensors="pt").cuda()
x = model.generate(x, max_new_tokens=512).cpu()
print(tok.batch_decode(x))

Prompt Template:

<|im_start|>system
You are an AI assistant.<|im_end|>
<|im_start|>user
Hi, how are you?<|im_end|>
<|im_start|>assistant
I'm doing well, thanks for asking!<|im_end|>
<|im_start|>user
Write me a poem about AI.<|im_end|>

Trained w/ Axolotl on 6x H100s for nine hours.

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