Mixtral-8x7B-Instruct-v0.1-FP8

Model Overview

• Model Architecture: Mixtral-8x7B-Instruct-v0.1
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Release Date: 3/6/2025
• Version: 1.0
• Model Developers: Neural Magic

Quantized version of Mixtral-8x7B-Instruct-v0.1. It achieves an average score of 72.66 on the OpenLLM benchmark (version 1), whereas the unquantized model achieves 73.44.

Model Optimizations

This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized, except the MLP routers.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

max_model_len, tp_size = 4096, 4
model_name = "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])

messages_list = [
    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
]

prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]

outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)

generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)

vLLM also supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created with llm-compressor by running the code snippet below with the following command:

python quantize.py --model_path mistralai/Mixtral-8x7B-Instruct-v0.1 --quant_path "output_dir" --calib_size 128 

import argparse
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.compression.helpers import calculate_offload_device_map
import torch
import os


def main():
    # Set up command line argument parsing
    parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
    parser.add_argument('--model_id', type=str, required=True,
                        help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")')
    parser.add_argument('--save_path', type=str, default='.',
                        help='Custom path to save the quantized model. If not provided, will use model_name-FP8')
    parser.add_argument('--calib_size', type=int, default=256)
    args = parser.parse_args()

    device_map = calculate_offload_device_map(
        args.model_id,
        reserve_for_hessians=False,
        num_gpus=torch.cuda.device_count(),
        trust_remote_code=True,
        torch_dtype=torch.bfloat16,
    )

    model = AutoModelForCausalLM.from_pretrained(
        args.model_id, device_map=device_map, torch_dtype=torch.bfloat16, trust_remote_code=True,
    )
    tokenizer = AutoTokenizer.from_pretrained(args.model_id)

    NUM_CALIBRATION_SAMPLES = args.calib_size
    DATASET_ID = "garage-bAInd/Open-Platypus"
    DATASET_SPLIT = "train"
    ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
    ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))

    def preprocess(example):
        concat_txt = example["instruction"] + "\n" + example["output"]
        return {"text": concat_txt}

    ds = ds.map(preprocess)

    def tokenize(sample):
        return tokenizer(
            sample["text"],
            padding=False,
            truncation=False,
            add_special_tokens=True,
        )

    ds = ds.map(tokenize, remove_columns=ds.column_names)

    # Configure the quantization algorithm and scheme
    recipe = QuantizationModifier(
        targets="Linear", scheme="FP8", ignore=["lm_head", "re:.*block_sparse_moe.gate"]
    )

    # Apply quantization
    oneshot(
        model=model,
        dataset=ds,
        recipe=recipe,
        num_calibration_samples=args.calib_size
    )

    save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8")
    os.makedirs(save_path, exist_ok=True)

    # Save to disk in compressed-tensors format
    model.save_pretrained(save_path, save_compressed=True, skip_compression_stats=True)
    tokenizer.save_pretrained(save_path)
    print(f"Model and tokenizer saved to: {save_path}")

if __name__ == "__main__":
    main()

Evaluation

The model was evaluated on OpenLLM Leaderboard V1 using the following command:

OpenLLM Leaderboard V1:

lm_eval \
  --model vllm \
  --model_args pretrained="neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=8,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
  --tasks openllm \
  --write_out \
  --batch_size auto \
  --output_path output_dir \
  --show_config

Accuracy

OpenLLM Leaderboard V1 evaluation scores

Metric	mistralai/Mixtral-8x7B-Instruct-v0.1	neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8
ARC-Challenge (Acc-Norm, 25-shot)	70.48	69.54
GSM8K (Strict-Match, 5-shot)	65.50	64.29
HellaSwag (Acc-Norm, 10-shot)	87.33	86.96
MMLU (Acc, 5-shot)	70.30	69.97
TruthfulQA (MC2, 0-shot)	64.81	63.89
Winogrande (Acc, 5-shot)	82.24	81.29
Average Score	73.44	72.66
Recovery (%)	100.00	98.94