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Mixtral-8x22B-v0.1 - FP8

Description

This repo contains the Mixtral-8x22B-v0.1 model quantized to FP8 by FriendliAI, significantly enhancing its inference efficiency while maintaining high accuracy. Note that FP8 is only supported by NVIDIA Ada, Hopper, and Blackwell GPU architectures. Check out FriendliAI documentation for more details.

Compatibility

This model is compatible with Friendli Container.

Prerequisites

  • Before you begin, make sure you have signed up for Friendli Suite. You can use Friendli Containers free of charge for four weeks.
  • Prepare a Personal Access Token following this guide.
  • Prepare a Friendli Container Secret following this guide.

Preparing Personal Access Token

PAT (Personal Access Token) is the user credential for for logging into our container registry.

  1. Sign in Friendli Suite.
  2. Go to User Settings > Tokens and click 'Create new token'.
  3. Save your created token value.

Pulling Friendli Container Image

  1. Log in to the Docker client using the personal access token created as outlined in this guide.
export FRIENDLI_PAT="YOUR PAT"
docker login registry.friendli.ai -u $YOUR_EMAIL -p $FRIENDLI_PAT
  1. Pull image
docker pull registry.friendli.ai/trial

Running Friendli Container

Once you've prepared the image of Friendli Container, you can launch it to create a serving endpoint.

docker run \
  --gpus '"device=0,1,2,3"' \
  -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -e FRIENDLI_CONTAINER_SECRET="YOUR CONTAINER SECRET" \
  registry.friendli.ai/trial \
    --web-server-port 8000 \
    --hf-model-name FriendliAI/Mixtral-8x22B-v0.1-fp8 \
    --num-devices 4  # Use tensor parallelism degree 4 

Optimizing Inference Performance with Policy Search

To serve MoE models efficiently, it is required to run a policy search to explore the optimal execution policy:

export POLICY_DIR=$PWD/policy

mkdir -p $POLICY_DIR

docker run \
  --gpus '"device=0,1,2,3"' \
  -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -v $POLICY_DIR:/policy \
  -e FRIENDLI_CONTAINER_SECRET="YOUR CONTAINER SECRET" \
  registry.friendli.ai/trial \
    --web-server-port 8000 \
    --hf-model-name FriendliAI/Mixtral-8x22B-v0.1-fp8 \
    --num-devices 4  # Use tensor parallelism degree 4 \
    --algo-policy-dir /policy \
    --search-policy true

When the optimal policy is successfully searched, the policy is compiled into a policy file and saved at $POLICY_DIR. Now you can create an inference endpoint with this optimal policy as follows:

docker run \
  --gpus '"device=0,1,2,3"' \
  -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -v $POLICY_DIR:/policy \
  -e FRIENDLI_CONTAINER_SECRET="YOUR CONTAINER SECRET" \
  registry.friendli.ai/trial \
    --web-server-port 8000 \
    --hf-model-name FriendliAI/Mixtral-8x22B-v0.1-fp8 \
    --num-devices 4  # Use tensor parallelism degree 4 \
    --algo-policy-dir /policy

Original model card: MistralAI's Mixtral-8x22B v0.1

Model Card for Mixtral-8x22B

The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts.

For full details of this model please read our release blog post.

Warning

This repo contains weights that are compatible with vLLM serving of the model as well as Hugging Face transformers library. It is based on the original Mixtral torrent release, but the file format and parameter names are different.

Run the model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mixtral-8x22B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id)

text = "Hello my name is"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:

Notice

Mixtral-8x22B is a pretrained base model and therefore does not have any moderation mechanisms.

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall

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