---
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
---

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png)

This model was trained on our [MetamathFewshot](https://huggingface.co/datasets/abacusai/MetaMathFewshot) dataset, as well as the [Vicuna](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) dataset and the [OrcaChat](https://huggingface.co/datasets/shahules786/orca-chat) dataset.

It has been finetuned from base [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)

# Usage

This model uses a specific prompt format which is encoded as a [chat template](https://huggingface.co/docs/transformers/main/en/chat_templating). To apply this, you can use the tokenizer.apply_chat_template() method of the attached tokenizer:

```python
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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_abacusai__Fewshot-Metamath-OrcaVicuna-Mistral)

|             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|