|
--- |
|
license: apache-2.0 |
|
language: |
|
- en |
|
- zh |
|
library_name: transformers |
|
widget: |
|
- text: "<s> [|User|] Hi 👋 </s>[|Assistant|]" |
|
--- |
|
|
|
## MiniChat-3B |
|
|
|
📑 [arXiv](https://arxiv.org/abs/2311.07052) | 👻 [GitHub](https://github.com/GeneZC/MiniMA) | 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) | 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) | 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) | 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B) |
|
|
|
❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2. |
|
|
|
A language model distilled and finetuned from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models". |
|
|
|
Outperforming a wide range of 3B competitors in GPT4 evaluation and even competing with several 7B chat models. |
|
|
|
<img src="./teaser_b.jpg" alt="teaser_b" width="687" /> |
|
|
|
The following is an example code snippet to use MiniChat-3B: |
|
|
|
```python |
|
import torch |
|
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
from conversation import get_default_conv_template |
|
|
|
# MiniChat |
|
tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B", use_fast=False) |
|
# GPU. |
|
model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval() |
|
# CPU. |
|
# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float32).eval() |
|
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
|
conv = get_default_conv_template("minichat") |
|
|
|
question = "Implement a program to find the common elements in two arrays without using any extra data structures." |
|
conv.append_message(conv.roles[0], question) |
|
conv.append_message(conv.roles[1], None) |
|
prompt = conv.get_prompt() |
|
input_ids = tokenizer([prompt]).input_ids |
|
output_ids = model.generate( |
|
torch.as_tensor(input_ids).to(device), |
|
do_sample=True, |
|
temperature=0.7, |
|
max_new_tokens=1024, |
|
) |
|
output_ids = output_ids[0][len(input_ids[0]):] |
|
output = tokenizer.decode(output_ids, skip_special_tokens=True).strip() |
|
# output: "def common_elements(arr1, arr2):\n if len(arr1) == 0:\n return []\n if len(arr2) == 0:\n return arr1\n\n common_elements = []\n for element in arr1:\n if element in arr2:\n common_elements.append(element)\n\n return common_elements" |
|
# Multiturn conversation could be realized by continuously appending questions to `conv`. |
|
``` |
|
|
|
## Bibtex |
|
|
|
```bibtex |
|
@article{zhang2023law, |
|
title={Towards the Law of Capacity Gap in Distilling Language Models}, |
|
author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan}, |
|
year={2023}, |
|
url={https://arxiv.org/abs/2311.07052} |
|
} |
|
``` |
|
# [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_GeneZC__MiniChat-3B) |
|
|
|
| Metric | Value | |
|
|-----------------------|---------------------------| |
|
| Avg. | 42.94 | |
|
| ARC (25-shot) | 44.03 | |
|
| HellaSwag (10-shot) | 67.19 | |
|
| MMLU (5-shot) | 39.17 | |
|
| TruthfulQA (0-shot) | 45.67 | |
|
| Winogrande (5-shot) | 65.27 | |
|
| GSM8K (5-shot) | 10.54 | |
|
| DROP (3-shot) | 28.73 | |
|
|