README.md

metadata

license: apache-2.0
datasets:
  - abacusai/MetaMathFewshot
  - shahules786/orca-chat
  - anon8231489123/ShareGPT_Vicuna_unfiltered
base_model: mistralai/Mistral-7B-v0.1
model-index:
  - name: Fewshot-Metamath-OrcaVicuna-Mistral
    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: 59.64
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abacusai/Fewshot-Metamath-OrcaVicuna-Mistral
          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: 81.82
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abacusai/Fewshot-Metamath-OrcaVicuna-Mistral
          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: 61.69
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abacusai/Fewshot-Metamath-OrcaVicuna-Mistral
          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: 53.23
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abacusai/Fewshot-Metamath-OrcaVicuna-Mistral
          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: 78.45
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abacusai/Fewshot-Metamath-OrcaVicuna-Mistral
          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: 69.14
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=abacusai/Fewshot-Metamath-OrcaVicuna-Mistral
          name: Open LLM Leaderboard

This model was trained on our MetamathFewshot dataset, as well as the Vicuna dataset and the OrcaChat dataset.

It has been finetuned from base Mistral 7B

Usage

This model uses a specific prompt format which is encoded as a chat template. To apply this, you can use the tokenizer.apply_chat_template() method of the attached tokenizer:

messages = [
    {"role": "user", "content": "What is the capital of Spain?"},
    {"role": "assistant", "content": "The capital of Spain is Madrid."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)

Evaluation Results

HuggingFace Leaderboard

Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
67.33	59.64	81.82	61.69	53.23	78.45	69.14

For comparison the GSM8K score for the original metamath/MetaMath-Mistral-7B was 68.84 and average score was 65.78.

MT-Bench

Turn 1	Turn 2	Average
6.90	6.52	6.71

Training Details

Instruction tuned with the following parameters:

• LORA, Rank 8, Alpha 16, Dropout 0.05, all modules (QKV and MLP)
• 3 epochs
• Micro Batch Size 32 over 4xH100, gradient accumulation steps = 1
• AdamW with learning rate 5e-5

Bias, Risks, and Limitations

The model has not been evaluated for safety and is only intended for research and experiments.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	67.33
AI2 Reasoning Challenge (25-Shot)	59.64
HellaSwag (10-Shot)	81.82
MMLU (5-Shot)	61.69
TruthfulQA (0-shot)	53.23
Winogrande (5-shot)	78.45
GSM8k (5-shot)	69.14