Mixtral 8x22B Instruct v0.1 EU AI Act Compliance Profile

Model Card for Mixtral-8x22B-Instruct-v0.1

Encode and Decode with mistral_common

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
 
mistral_models_path = "MISTRAL_MODELS_PATH"
 
tokenizer = MistralTokenizer.v3()
 
completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
 
tokens = tokenizer.encode_chat_completion(completion_request).tokens

Inference with mistral_inference

from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate

model = Transformer.from_folder(mistral_models_path)
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)

result = tokenizer.decode(out_tokens[0])

print(result)

Preparing inputs with Hugging Face transformers

from transformers import AutoTokenizer

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

chat = [{"role": "user", "content": "Explain Machine Learning to me in a nutshell."}]

tokens = tokenizer.apply_chat_template(chat, return_dict=True, return_tensors="pt", add_generation_prompt=True)

Inference with hugging face transformers

from transformers import AutoModelForCausalLM
import torch

# You can also use 8-bit or 4-bit quantization here
model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1", torch_dtype=torch.bfloat16, device_map="auto")
model.to("cuda")
 
generated_ids = model.generate(**tokens, max_new_tokens=1000, do_sample=True)

# decode with HF tokenizer
result = tokenizer.decode(generated_ids[0])
print(result)

[!TIP] PRs to correct the transformers tokenizer so that it gives 1-to-1 the same results as the mistral_common refere

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