Instructions to use QuixiAI/DeepMixtral-8x7b-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuixiAI/DeepMixtral-8x7b-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="QuixiAI/DeepMixtral-8x7b-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("QuixiAI/DeepMixtral-8x7b-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuixiAI/DeepMixtral-8x7b-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuixiAI/DeepMixtral-8x7b-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuixiAI/DeepMixtral-8x7b-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuixiAI/DeepMixtral-8x7b-Instruct

SGLang

How to use QuixiAI/DeepMixtral-8x7b-Instruct 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 "QuixiAI/DeepMixtral-8x7b-Instruct" \
    --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": "QuixiAI/DeepMixtral-8x7b-Instruct",
		"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 "QuixiAI/DeepMixtral-8x7b-Instruct" \
        --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": "QuixiAI/DeepMixtral-8x7b-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuixiAI/DeepMixtral-8x7b-Instruct with Docker Model Runner:
```
docker model run hf.co/QuixiAI/DeepMixtral-8x7b-Instruct
```

Crystalcareai commited on May 5, 2024

Commit

c7ac1fb

verified ·

1 Parent(s): 14ac240

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -6,7 +6,7 @@ language:
 <p align="center"> <img src="https://cdn-lfs-us-1.huggingface.co/repos/58/11/5811c78d8fc8a7e29f637f442dc17b5fdc3ee97e6ce5e3ead6c9eaeed704e08f/12a4f1bdfdaabdc5114d8e72465b60c97c5e2037a7d5c22ff5fd53cfa80e58ab?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27DeepSeek-Mixtral.png%3B+filename%3D%22DeepSeek-Mixtral.png%22%3B&response-content-type=image%2Fpng&Expires=1715149068&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNTE0OTA2OH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzU4LzExLzU4MTFjNzhkOGZjOGE3ZTI5ZjYzN2Y0NDJkYzE3YjVmZGMzZWU5N2U2Y2U1ZTNlYWQ2YzllYWVlZDcwNGUwOGYvMTJhNGYxYmRmZGFhYmRjNTExNGQ4ZTcyNDY1YjYwYzk3YzVlMjAzN2E3ZDVjMjJmZjVmZDUzY2ZhODBlNThhYj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=RZNhKGJhLpnd2j%7EeImEHG8wBlntw9yJ6xJcNcbQXdngDetFhqFK46fJ3ndgzAoxbgwSHrgpYTdAR9ZSzinuY8TuvUgXEX64dZhvmgLIzcfdqfMIKOOg4XME45rZpWdQApAn%7EsSGNNwJPGvXh3MHXPjo0fOxiCf5zSPNl342EInA8FY%7E2jXEykwrfAK5OBWpbEi65WSbBSs6r3ob-66dURDEKfvfPN22VMvAYfiBiajvo6tQcL8cQOK5BWeQcsAZCDOTSxljD8--g2nXU2pl5WXh6Kv74szFWA4zEL7GOaZLRNdcTUHQmxen6144xngrv%7ERnd2jTRNCpH27M7rbGpvA__&Key-Pair-Id=KCD77M1F0VK2B" width="auto" title="LlaMoE-Medium model image"> </p>
-## DeepSeek-MoE Architecture Integration
 This is a direct extraction of the 8 experts from [Mixtral-8x7b-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), and placing them into the DeepSeek-MoE Architecture.

 <p align="center"> <img src="https://cdn-lfs-us-1.huggingface.co/repos/58/11/5811c78d8fc8a7e29f637f442dc17b5fdc3ee97e6ce5e3ead6c9eaeed704e08f/12a4f1bdfdaabdc5114d8e72465b60c97c5e2037a7d5c22ff5fd53cfa80e58ab?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27DeepSeek-Mixtral.png%3B+filename%3D%22DeepSeek-Mixtral.png%22%3B&response-content-type=image%2Fpng&Expires=1715149068&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcxNTE0OTA2OH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzU4LzExLzU4MTFjNzhkOGZjOGE3ZTI5ZjYzN2Y0NDJkYzE3YjVmZGMzZWU5N2U2Y2U1ZTNlYWQ2YzllYWVlZDcwNGUwOGYvMTJhNGYxYmRmZGFhYmRjNTExNGQ4ZTcyNDY1YjYwYzk3YzVlMjAzN2E3ZDVjMjJmZjVmZDUzY2ZhODBlNThhYj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=RZNhKGJhLpnd2j%7EeImEHG8wBlntw9yJ6xJcNcbQXdngDetFhqFK46fJ3ndgzAoxbgwSHrgpYTdAR9ZSzinuY8TuvUgXEX64dZhvmgLIzcfdqfMIKOOg4XME45rZpWdQApAn%7EsSGNNwJPGvXh3MHXPjo0fOxiCf5zSPNl342EInA8FY%7E2jXEykwrfAK5OBWpbEi65WSbBSs6r3ob-66dURDEKfvfPN22VMvAYfiBiajvo6tQcL8cQOK5BWeQcsAZCDOTSxljD8--g2nXU2pl5WXh6Kv74szFWA4zEL7GOaZLRNdcTUHQmxen6144xngrv%7ERnd2jTRNCpH27M7rbGpvA__&Key-Pair-Id=KCD77M1F0VK2B" width="auto" title="LlaMoE-Medium model image"> </p>
+## Mixtral Experts with DeepSeek-MoE Architecture
 This is a direct extraction of the 8 experts from [Mixtral-8x7b-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), and placing them into the DeepSeek-MoE Architecture.