README.md

metadata

language:
  - en
license: apache-2.0
library_name: transformers
datasets:
  - monology/VMware-open-instruct-higgsfield
pipeline_tag: text-generation
model-index:
  - name: openinstruct-mistral-7b
    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
            value: 59.73
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=monology/openinstruct-mistral-7b
          name: Open LLM Leaderboard
      - 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
            value: 82.77
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=monology/openinstruct-mistral-7b
          name: Open LLM Leaderboard
      - 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
            value: 60.55
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=monology/openinstruct-mistral-7b
          name: Open LLM Leaderboard
      - 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: 48.76
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=monology/openinstruct-mistral-7b
          name: Open LLM Leaderboard
      - 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
            value: 79.56
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=monology/openinstruct-mistral-7b
          name: Open LLM Leaderboard
      - 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
            value: 50.49
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=monology/openinstruct-mistral-7b
          name: Open LLM Leaderboard

OpenInstruct Mistral-7B

1st among commercially-usable 7B models on the Open LLM Leaderboard!*

This is mistralai/Mistral-7B-v0.1 finetuned on VMware/open-instruct.
Quantized to FP16 and released under the Apache-2.0 license by myself.
Compute generously provided by Higgsfield AI.

Prompt format: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
[your instruction goes here]

### Response:

Recommended preset:

• temperature: 0.2
• top_k: 50
• top_p 0.95
• repetition_penalty: 1.1

*as of 21 Nov 2023. "commercially-usable" includes both an open-source base model and a non-synthetic open-source finetune dataset. updated leaderboard results available here.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	63.64
AI2 Reasoning Challenge (25-Shot)	59.73
HellaSwag (10-Shot)	82.77
MMLU (5-Shot)	60.55
TruthfulQA (0-shot)	48.76
Winogrande (5-shot)	79.56
GSM8k (5-shot)	50.49