Instructions to use Inferless/Mixtral-8x7B-v0.1-int8-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Inferless/Mixtral-8x7B-v0.1-int8-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="Inferless/Mixtral-8x7B-v0.1-int8-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Inferless/Mixtral-8x7B-v0.1-int8-GPTQ")
model = AutoModelForCausalLM.from_pretrained("Inferless/Mixtral-8x7B-v0.1-int8-GPTQ")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Inferless/Mixtral-8x7B-v0.1-int8-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Inferless/Mixtral-8x7B-v0.1-int8-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Inferless/Mixtral-8x7B-v0.1-int8-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Inferless/Mixtral-8x7B-v0.1-int8-GPTQ

SGLang

How to use Inferless/Mixtral-8x7B-v0.1-int8-GPTQ 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 "Inferless/Mixtral-8x7B-v0.1-int8-GPTQ" \
    --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": "Inferless/Mixtral-8x7B-v0.1-int8-GPTQ",
		"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 "Inferless/Mixtral-8x7B-v0.1-int8-GPTQ" \
        --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": "Inferless/Mixtral-8x7B-v0.1-int8-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Inferless/Mixtral-8x7B-v0.1-int8-GPTQ with Docker Model Runner:
```
docker model run hf.co/Inferless/Mixtral-8x7B-v0.1-int8-GPTQ
```

Serverless GPUs to scale your machine learning inference without any hassle of managing servers, deploy complicated and custom models with ease.

Join Private Beta

Go through this tutorial, for quickly deploy Mixtral-8x7B-v0.1 using Inferless

Mixtral-8x7B - GPTQ

Model creator: Mistralai
Original model: Mixtral-8x7B-v0.1

Description

This repo contains GPTQ model files for Mistralai's Mixtral-8x7B-v0.1.

About GPTQ

GPTQ is a method that compresses the model size and accelerates inference by quantizing weights based on a calibration dataset, aiming to minimize mean squared error in a single post-quantization step. GPTQ achieves both memory efficiency and faster inference.

It is supported by:

Text Generation Webui - using Loader: AutoAWQ
vLLM - version 0.2.2 or later for support for all model types.
Hugging Face Text Generation Inference (TGI)
Transformers version 4.35.0 and later, from any code or client that supports Transformers
AutoAWQ - for use from Python code

Shared files, and GPTQ parameters

Models are released as sharded safetensors files.

Branch	Bits	GS	AWQ Dataset	Seq Len	Size
main	4	128	VMware Open Instruct	4096	5.96 GB

How to use

You will need the following software packages and python libraries:

build:
  cuda_version: "12.1.1"
  system_packages:
    - "libssl-dev"
  python_packages:
    - "torch==2.1.2"
    - "vllm==0.2.6"
    - "transformers==4.36.2"
    - "accelerate==0.25.0"

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