README.md

metadata

license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
model-index:
  - name: Mistral-7B-Instruct-v0.3-GPTQ-4bit
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            name: normalized accuracy
            value: 63.4
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            name: normalized accuracy
            value: 84.04
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 57.48
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            name: accuracy
            value: 45.41
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            name: accuracy
            value: 61.07
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            name: accuracy
            value: 79.08

Model Card for Mistral-7B-Instruct-v0.3 quantized to 4bit weights

• Weight-only quantization of Mistral-7B-Instruct-v0.3 via GPTQ to 4bits with group_size=128
• GPTQ optimized for 99.75% accuracy recovery relative to the unquantized model

Open LLM Leaderboard evaluation scores

	Mistral-7B-Instruct-v0.3	Mistral-7B-Instruct-v0.3-GPTQ-4bit (this model)
arc-c 25-shot	63.48	63.40
mmlu 5-shot	61.13	60.89
hellaswag 10-shot	84.49	84.04
winogrande 5-shot	79.16	79.08
gsm8k 5-shot	43.37	45.41
truthfulqa 0-shot	59.65	57.48
Average Accuracy	65.21	65.05
Recovery	100%	99.75%

vLLM Inference Performance

This model is ready for optimized inference using the Marlin mixed-precision kernels in vLLM: https://github.com/vllm-project/vllm

Simply start this model as an inference server with:

python -m vllm.entrypoints.openai.api_server --model neuralmagic/Mistral-7B-Instruct-v0.3-GPTQ-4bit