Mistral-7B-Instruct-v0.2-fp8

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Mistral-7B-Instruct-v0.2 - FP8

• Model creator: Mistral AI
• Original model: Mistral-7B-Instruct-v0.2

Description

This repo contains the Mistral-7B-Instruct-v0.2 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.

License

Refer to the license of the original model card.

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.

Preparing Container Secret

Container secret is a credential to launch our Friendli Container images. You should pass the container secret as an environment variable to run the container image.

1. Sign in Friendli Suite.
2. Go to Container > Container Secrets and click 'Create secret'.
3. Save your created secret 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"' \
  -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/Mistral-7B-Instruct-v0.2-fp8

---

Original model card: Mistral AI's Mistral-7B-Instruct-v0.2

Model Card for Mistral-7B-Instruct-v0.2

The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.2.

Mistral-7B-v0.2 has the following changes compared to Mistral-7B-v0.1

• 32k context window (vs 8k context in v0.1)
• Rope-theta = 1e6
• No Sliding-Window Attention

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

Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by [INST] and [/INST] tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.

E.g.

text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"

This format is available as a chat template via the apply_chat_template() method:

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

Troubleshooting

• If you see the following error:

Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'

Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers

This should not be required after transformers-v4.33.4.

Limitations

The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.

The Mistral AI Team

Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.