---
language:
- en
license: llama2
datasets:
- umd-zhou-lab/claude2_alpaca
model-index:
- name: claude2-alpaca-13B
  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: 61.18
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=umd-zhou-lab/claude2-alpaca-13B
      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: 84.21
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=umd-zhou-lab/claude2-alpaca-13B
      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: 55.93
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=umd-zhou-lab/claude2-alpaca-13B
      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: 45.02
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=umd-zhou-lab/claude2-alpaca-13B
      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: 76.8
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=umd-zhou-lab/claude2-alpaca-13B
      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: 28.28
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=umd-zhou-lab/claude2-alpaca-13B
      name: Open LLM Leaderboard
---
# Model Card for umd-zhou-lab/claude2-alpaca-13B

<!-- Provide a quick summary of what the model is/does. -->

This model is trained by fine-tuning llama-2 with claude2 alpaca data.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->


- **Developed by:** UMD Tianyi Zhou Lab
- **Model type:** An auto-regressive language model based on the transformer architecture
- **License:** Llama 2 Community License Agreement
- **Finetuned from model:** [meta-llama/Llama-2-13b](https://huggingface.co./meta-llama/Llama-2-13b)

### Model Sources

<!-- Provide the basic links for the model. -->

- **GitHub:** [Claude2-Alpaca](https://github.com/Lichang-Chen/claude2-alpaca)
- **Data:** [claude2_alpaca](https://huggingface.co./datasets/umd-zhou-lab/claude2_alpaca)

## Uses

The primary use of this model is research on large language models and chatbots. 
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

## Training

We use the prompt from [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)


| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
| --- | ---: | ---: | ---: | ---: | ---: |
| Model (13B) | 128 | 1e-5 | 5 | 2048 | 0 |

## Performance

Compared to the llama2-chat, our models can have better average performance.<br>

|                    | Average | ARC  | HellaSwag | MMLU  | TruthfulQA | Alpaca_Eval | Avg Length |
|---|---|---|---|---|---|---|---|
| Llama-2-7b-chat | 56.335  | 52.9 | 78.55     | 48.32 | 45.57      | 71.37       | 1479       |
| Llama-2-13b-chat   | 59.935  | 59.04| 81.94     | 54.64 | 44.12      | 81.09       | 1513       |
|||||||||
| claude_alpaca-7b  | 57.78   | 56.66 | 81.17     | 46.58 | 46.71      | 71.23       | 1066       |
| claude_alpaca-13b | 61.29   | 61.18 | 84.08     | 55.74 | 44.18      | 78.93       | 1127       |


## Citation

Please consider citing our paper if you think our codes, data, or models are useful. Thank you!
```
@misc{claude2-alpaca,
  author = {Lichang Chen and Khalid Saifullah and Ming Li and Tianyi Zhou and Heng Huang},
  title = {Claude2-Alpaca: Instruction tuning datasets distilled from claude},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Lichang-Chen/claude2-alpaca}},
}
```
# [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_umd-zhou-lab__claude2-alpaca-13B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |58.57|
|AI2 Reasoning Challenge (25-Shot)|61.18|
|HellaSwag (10-Shot)              |84.21|
|MMLU (5-Shot)                    |55.93|
|TruthfulQA (0-shot)              |45.02|
|Winogrande (5-shot)              |76.80|
|GSM8k (5-shot)                   |28.28|