---
language:
- en
license: apache-2.0
library_name: transformers
datasets:
- Open-Orca/SlimOrca
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- meta-math/MetaMathQA
pipeline_tag: text-generation
arxiv: 2401.02731
model-index:
- name: Camelidae-8x7B
  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: 55.63
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=hywu/Camelidae-8x7B
      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: 79.18
      name: normalized accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=hywu/Camelidae-8x7B
      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: 50.1
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=hywu/Camelidae-8x7B
      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: 42.86
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=hywu/Camelidae-8x7B
      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: 76.24
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=hywu/Camelidae-8x7B
      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: 22.82
      name: accuracy
    source:
      url: https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=hywu/Camelidae-8x7B
      name: Open LLM Leaderboard
---


# Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks

## News
- 1/10/2024 - Camelidae models are now available on [🤗HuggingFace](https://huggingface.co./hywu).
- 1/4/2024 - We released the paper, [Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks](https://arxiv.org/abs/2401.02731).
- 12/22/2023 - We released the training [repo](https://github.com/wuhy68/Parameter-Efficient-MoE) that craft the dense model with LLaMA architecture to the MoE model.

## Introduction
Camelidae models are trained utilizing Parameter-Efficient Sparsity Crafting techniques

Parameter-Efficient Sparsity Crafting can help dense models learn knowledge from different fields (including code and math). This appraoch perfrom instruction tuning and utilize MoE structure in an efficient way.

Specifically, Parameter-Efficient Sparsity Crafting utilizes parameter efficient techiniques including [QLoRA](https://arxiv.org/abs/2305.14314) and [Adapter](https://arxiv.org/abs/1902.00751) to perfrom Efficient [Sparse Upcycling](https://arxiv.org/abs/2212.05055).

## Model Lists
| Model | Download  
|---|---
Camelidae-8x7B   | [🤗HuggingFace](https://huggingface.co./hywu/Camelidae-8x7B)
Camelidae-8x13B  | [🤗HuggingFace](https://huggingface.co./hywu/Camelidae-8x13B)
Camelidae-8x34B  | [🤗HuggingFace](https://huggingface.co./hywu/Camelidae-8x34B) 

## Performance
| Model | MMLU (5shot) | GSM8k (5shot) | MATH (4shot) | HumanEval (0shot) | MBPP (4shot) | HellaSwag (10shot) | TriviaQA (0shot) |
|----------------------:|:------------:|:-------------:|:------------:|:-----------------:|:------------:|:------------------:|:----------------:|
| GPT3.5 | 70.0% | 57.1% | **34.1%** | **48.1%** | - | 85.5% | - |
| Camelidae-8x34B | 75.6% | **78.3%** | **22.6%** | **43.9%** | **41.4%** | 85.3% | **63.4%** |
| SUSChat-34B | **76.4%** | 72.3% | 22.0% | 11.6% | 40.2% | 83.9% | 56.1% |
| Mixtral-8x7B-instruct | 68.7% | 71.7% | 22.1% | 25.6% | 40.6% | **86.5%** | 57.7% |
| LLaMA2-70B-chat | 63.8% | 59.3% | 10.4% | 32.3% | 35.6% | 84.8% | 63.0% |
| Camelidae-8x13B | 54.4% | 52.6% | 9.8% | 30.6% | 30.4% | 82.5% | 59.4% |
| LLaMA2-13B-chat | 53.9% | 37.1% | 5.2% | 18.9% | 27.2% | 81.9% | 55.0% |
| Camelidae-8x7B | 48.3% | 44.0% | 5.8% | 18.3% | 23.4% | 79.2% | 51.0% |
| LLaMA2-7B-chat | 47.2% | 26.3% | 3.9% | 12.2% | 17.6% | 78.6% | 46.4% |

We bold the highest scores for open-source models and all models separately.


## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x7B", trust_remote_code=True)
# tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x13B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("hywu/Camelidae-8x34B", trust_remote_code=True)

# model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x7B", device_map="auto", trust_remote_code=True).eval()
# model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x13B", device_map="auto", trust_remote_code=True).eval()
model = AutoModelForCausalLM.from_pretrained("hywu/Camelidae-8x34B", device_map="auto", trust_remote_code=True).eval()

inputs = tokenizer('### Human:\nHow are you?\n### Assistant:\n', return_tensors='pt')
inputs = inputs.to(model.device)
pred = model.generate(**inputs)
print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
# I am doing well, thank you.
```

## Citation
```bibtex
@article{wu2024parameter,
  title={Parameter-Efficient Sparsity Crafting from Dense to Mixture-of-Experts for Instruction Tuning on General Tasks},
  author={Wu, Haoyuan and Zheng, Haisheng and Yu, Bei},
  journal={arXiv preprint arXiv:2401.02731},
  year={2024}
}
```

## License
The source code in this repo is licensed under the [Apache 2.0 License](https://github.com/wuhy68/Parameter-Efficient-MoE/blob/master/LICENSE). Camelidae models are developed for academic research and free commercial use, all usage must adhere to the license from [facebookresearch](https://github.com/facebookresearch/llama/blob/main/LICENSE) and [01-ai](https://github.com/01-ai/Yi/blob/main/MODEL_LICENSE_AGREEMENT.txt).
# [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_hywu__Camelidae-8x7B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |54.47|
|AI2 Reasoning Challenge (25-Shot)|55.63|
|HellaSwag (10-Shot)              |79.18|
|MMLU (5-Shot)                    |50.10|
|TruthfulQA (0-shot)              |42.86|
|Winogrande (5-shot)              |76.24|
|GSM8k (5-shot)                   |22.82|