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https://github.com/freedomintelligence/llmzoo
⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡
https://github.com/freedomintelligence/llmzoo
Last synced: about 2 months ago
JSON representation
⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡
- Host: GitHub
- URL: https://github.com/freedomintelligence/llmzoo
- Owner: FreedomIntelligence
- License: apache-2.0
- Created: 2023-04-01T12:08:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-26T17:48:30.000Z (10 months ago)
- Last Synced: 2024-08-01T11:15:52.727Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 4.65 MB
- Stars: 2,907
- Watchers: 51
- Forks: 198
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - LLMZoo - ⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡ (NLP)
README
# LLM Zoo: democratizing ChatGPT
⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡ [[Tech Report]](assets/llmzoo.pdf)
## ✨ Latest News
- [07/12/2023]: More instruction-following data of different languages is available [here](https://github.com/FreedomIntelligence/MultilingualSIFT).
- [05/05/2023]: Release the training code. Now, you can replicate a multilingual instruction-following LLM by yourself. :-)
- [04/24/2023]: Add more results (e.g., MOSS) in the evaluation benchmark.
- [04/08/2023]: Release the Phoenix (for all languages) and Chimera (for Latin languages) models.
## 🤔 Motivation
- Break "AI supremacy" and democratize ChatGPT
> "AI supremacy" is understood as a company's absolute leadership and monopoly position in an AI field, which may even include exclusive capabilities beyond general artificial intelligence. This is unacceptable for AI community and may even lead to individual influence on the direction of the human future, thus bringing various hazards to human society.
- Make ChatGPT-like LLM accessible across countries and languages
- Make AI open again. Every person, regardless of their skin color or place of birth, should have equal access to the
technology gifted by the creator. For example, many pioneers have made great efforts to spread the use of light bulbs
and vaccines to developing countries. Similarly, ChatGPT, one of the greatest technological advancements in modern
history, should also be made available to all.
## 🎬 Get started
### Install
Run the following command to install the required packages:
```angular2html
pip install -r requirements.txt
```
### CLI Inference
```bash
python -m llmzoo.deploy.cli --model-path /path/to/weights/
```
For example, for `Phoenix`, run
```bash
python -m llmzoo.deploy.cli --model-path FreedomIntelligence/phoenix-inst-chat-7b
```
and it will download the model from Hugging Face automatically. For `Chimera`, please follow this [instruction](https://github.com/FreedomIntelligence/LLMZoo#chimera-llm-mainly-for-latin-and-cyrillic-languages) to prepare the weights.
> Check [here](#-deployment) for deploying a web application.
## 📚 Data
### Overview
We used the following two types of data for training `Phoenix` and `Chimera`:
Instruction data
- Multilingual instructions (language-agnostic instructions with post-translation)
```diff
+ Self-Instructed / Translated (Instruction, Input) in Language A
- ---(Step 1) Translation --->
+ (Instruction, Input) in Language B (B is randomly sampled w.r.t. the probability distribution of realistic languages)
- ---(Step 2) Generate--->
+ Output in Language B
```
- User-centered instructions
```diff
+ (Role, Instruction, Input) seeds
- ---(Step 1) Self Instruct--->
+ (Role, Instruction, Input) samples
- ---(Step 2) generate output Instruct--->
+ (Role, Instruction, Input) ---> Output
```
Conversation data
- User-shared conversations
```diff
+ ChatGPT conversations shared on the Internet
- ---(Step 1) Crawl--->
+ Multi-round conversation data
```
> Check [InstructionZoo](https://github.com/FreedomIntelligence/InstructionZoo) for the collection of instruction datasets.
> Check [GPT-API-Accelerate Tool](https://github.com/FreedomIntelligence/GPT-API-Accelerate) for faster data generation using ChatGPT.
### Download
- [phoenix-sft-data-v1](https://huggingface.co/datasets/FreedomIntelligence/phoenix-sft-data-v1): The data used for training Phoenix and Chimera.
## 🐼 Models
### Overview of existing models
| Model | Backbone | #Params | Open-source model | Open-source data | Claimed language | Post-training (instruction) | Post-training (conversation) | Release date |
|-------------------------------|----------|---------:|------------------:|-----------------:|-----------------:|----------------------------:|-----------------------------:|-------------:|
| ChatGPT | - | - | ❌ | ❌ | multi | | | 11/30/22 |
| Wenxin | - | - | ❌ | ❌ | zh | | | 03/16/23 |
| ChatGLM | GLM | 6B | ✅ | ❌ | en, zh | | | 03/16/23 |
| Alpaca | LLaMA | 7B | ✅ | ✅ | en | 52K, en | ❌ | 03/13/23 |
| Dolly | GPT-J | 6B | ✅ | ✅ | en | 52K, en | ❌ | 03/24/23 |
| BELLE | BLOOMZ | 7B | ✅ | ✅ | zh | 1.5M, zh | ❌ | 03/26/23 |
| Guanaco | LLaMA | 7B | ✅ | ✅ | en, zh, ja, de | 534K, multi | ❌ | 03/26/23 |
| Chinese-LLaMA-Alpaca | LLaMA | 7/13B | ✅ | ✅ | en, zh | 2M/3M, en/zh | ❌ | 03/28/23 |
| LuoTuo | LLaMA | 7B | ✅ | ✅ | zh | 52K, zh | ❌ | 03/31/23 |
| Vicuna | LLaMA | 7/13B | ✅ | ✅ | en | ❌ | 70K, multi | 03/13/23 |
| Koala | LLaMA | 13B | ✅ | ✅ | en | 355K, en | 117K, en | 04/03/23 |
| BAIZE | LLaMA | 7/13/30B | ✅ | ✅ | en | 52K, en | 111.5K, en | 04/04/23 |
| Phoenix (Ours) | BLOOMZ | 7B | ✅ | ✅ | multi | 40+ | 40+ | 04/08/23 |
| Latin Phoenix: Chimera (Ours) | LLaMA | 7/13B | ✅ | ✅ | multi (Latin) | Latin | Latin | 04/08/23 |
The key difference between existing models and ours.
> The key difference in our models is that we utilize two sets of data, namely **instructions** and **conversations**, which were previously only used by Alpaca and Vicuna respectively. We believe that incorporating both types of data is essential for a recipe to achieve a proficient language model. The rationale is that *the **instruction** data helps to tame language models to adhere to human instructions and fulfill their information requirements*, while *the **conversation** data facilitates the development of conversational skills in the model*. Together, these two types of data complement each other to create a more well-rounded language model.
### Phoenix (LLM across Languages)
The philosophy to name
> The first model is named **Phoenix**. In Chinese culture, the Phoenix is commonly regarded as a symbol of *the king of birds*; as the saying goes "百鸟朝凤", indicating its ability to coordinate with all birds, even if they speak different languages. We refer to Phoenix as the one capable of understanding and speaking hundreds of (bird) languages. More importantly, **Phoenix** is the totem of "the Chinese University of Hong Kong, Shenzhen" (CUHKSZ); it goes without saying this is also for the Chinese University of Hong Kong (CUHK).
| Model | Backbone | Data | Link |
|----------------------|---------------|--------------|-------------------------------------------------------------------------------|
| Phoenix-chat-7b | BLOOMZ-7b1-mt | Conversation | [parameters](https://huggingface.co/FreedomIntelligence/phoenix-chat-7b) |
| Phoenix-inst-chat-7b | BLOOMZ-7b1-mt | Instruction + Conversation | [parameters](https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b) |
| Phoenix-inst-chat-7b-int4 | BLOOMZ-7b1-mt | Instruction + Conversation | [parameters](https://huggingface.co/FreedomIntelligence/phoenix-inst-chat-7b-int4) |
### Chimera (LLM mainly for Latin and Cyrillic languages)
The philosophy to name
> The philosophy to name: The biggest barrier to LLM is that we do not have enough candidate names for LLMs, as LLAMA, Guanaco, Vicuna, and Alpaca have already been used, and there are no more members in the camel family. Therefore, we find a similar hybrid creature in Greek mythology, [Chimera](https://en.wikipedia.org/wiki/Chimera_(mythology)), composed of different Lycia and Asia Minor animal parts. Coincidentally, it is a hero/role in DOTA (and also Warcraft III). It could therefore be used to memorize a period of playing games overnight during high school and undergraduate time.
| Model | Backbone | Data | Link |
|-----------------------|-----------|----------------------------|----------------------------------------------------------------------------------------------|
| Chimera-chat-7b | LLaMA-7b | Conversation | [parameters (delta)](https://huggingface.co/FreedomIntelligence/chimera-chat-7b-delta) |
| Chimera-chat-13b | LLaMA-13b | Conversation | [parameters (delta)](https://huggingface.co/FreedomIntelligence/chimera-chat-13b-delta) |
| Chimera-inst-chat-7b | LLaMA-7b | Instruction + Conversation | [parameters (delta)](https://huggingface.co/FreedomIntelligence/chimera-inst-chat-7b-delta) |
| Chimera-inst-chat-13b | LLaMA-13b | Instruction + Conversation | [parameters (delta)](https://huggingface.co/FreedomIntelligence/chimera-inst-chat-13b-delta) |
Due to LLaMA's license restrictions, we follow [FastChat](https://github.com/lm-sys/FastChat) to release our delta weights. To use Chimera, download the original [LLaMA weights](https://huggingface.co/docs/transformers/main/model_doc/llama) and run the script:
```bash
python tools/apply_delta.py \
--base /path/to/llama-13b \
--target /output/path/to/chimera-inst-chat-13b \
--delta FreedomIntelligence/chimera-inst-chat-13b-delta
```
### CAMEL (Chinese And Medically Enhanced Langauge models)
The philosophy to name
> The philosophy to name: Its Chinese name is [HuatuoGPT](https://github.com/FreedomIntelligence/HuatuoGPT) or 华佗GPT to commemorate the great Chinese physician named Hua Tuo (华佗), who lived around 200 AC. Training is already finished; we will release it in two weeks; some efforts are needed to deploy it in public cloud servers in case of massive requests.
Check our models in [HuatuoGPT](https://github.com/FreedomIntelligence/HuatuoGPT) or try our [demo](https://www.huatuogpt.cn/) .
Similar biomedical models could be seen in [biomedical LLMs](assets/biomedical-models.md).
More models in the future
### Legal GPT (coming soon)
### Vision-Language Models (coming soon)
### Retrieval-augmented Models (coming soon)
## 🧐 Evaluation and Benchmark
We provide a bilingual, multidimensional comparison across different open-source models with ours.
### Chinese
* Automatic Evaluation Using GPT-4:
| Model | Ratio |
|-------------------------------------------------|---------|
| Phoenix-inst-chat-7b vs. **ChatGPT** | 85.2\% |
| Phoenix-inst-chat-7b vs. **ChatGLM-6b** | 94.6\% |
| Phoenix-inst-chat-7b vs. **Baidu-Wenxin** | 96.8\% |
| **Phoenix-inst-chat-7b** vs. MOSS-moon-003-sft | 109.7\% |
| **Phoenix-inst-chat-7b** vs. BELLE-7b-2m | 122.7\% |
| **Phoenix-inst-chat-7b** vs. Chinese-Alpaca-7b | 135.3\% |
| **Phoenix-inst-chat-7b** vs. Chinese-Alpaca-13b | 125.2\% |
**Observation**: It shows that Phoenix-chat-7b achieves 85.2\% performance of ChatGPT in Chinese. It slightly underperforms Baidu-Wenxin (96.8\%) and ChatGLM-6b (94.6 \%), both are not fully open-source; ChatGLM-6b only provides model weights without training data and details. Although Phoenix is a multilingual LLM, it achieves SOTA performance among all open-source Chinese LLMs.
* Human Evaluation:
| | win | tie | lose |
|--------------------------------------|:---:|:---:|:-----:|
| Phoenix vs. **ChatGPT** | 12 | 35 | 53 |
| Phoenix vs. **ChatGLM-6b** | 36 | 11 | 53 |
| Phoenix vs. **Baidu-Wenxin** | 29 | 25 | 46 |
| **Phoenix** vs. BELLE-7b-2m | 55 | 31 | 14 |
| **Phoenix** vs. Chinese-Alpaca-13b | 56 | 31 | 13 |
**Observation**: It shows that the human evaluation results show the same trend as the automatic evaluation results.
### English
* Automatic Evaluation Using GPT-4:
| Model | Ratio |
|----------------------------------------|------------|
| Chimera-chat-7b vs. **ChatGPT** | 85.2\% |
| Chimera-chat-13b vs. **ChatGPT** | 92.6\% |
| Chimera-inst-chat-13b vs. **ChatGPT** | **96.6\%** |
## 👾 Quantization
We offer int8 and int4 quantizations, which will largely reduce the GPU memory consumption, e.g., from ~28GB to ~7GB for phoenix.
### Int8
You can directly obatin int8 version of phoenix by passing `--load-8bit` when using cli inference. E.g.,
```bash
python -m llmzoo.deploy.cli --model-path FreedomIntelligence/phoenix-inst-chat-7b --load-8bit
```
### Int4
For int4 version, we take advantage of GPTQ. You can directly obatin int4 version of `Phoenix` by passing int4 version model and `--load-4bit` when using cli inference. This would require package `AutoGPTQ` be installed. E.g.,
```bash
python -m llmzoo.deploy.cli --model-path FreedomIntelligence/phoenix-inst-chat-7b-int4 --load-4bit
```
We use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) to support `Phoenix` via,
```bash
BUILD_CUDA_EXT=0 pip install auto-gptq[triton]
```
For `Chimera`, we can not share the int4 version parameters due to restrictions. And you can follow the example in our patched [AutoGPTQ](https://github.com/GeneZC/AutoGPTQ-triton/tree/main/examples) to conduct quantization by yourselves.
Thank [yhyu13](https://github.com/yhyu13), please check the merged weight and GPTQ quantized weight for chimera in [chimera-inst-chat-13b-hf](https://huggingface.co/Yhyu13/) and [chimera-inst-chat-13b-gptq-4bit](https://huggingface.co/Yhyu13/chimera-inst-chat-13b-gptq-4bit).
**Inference in pure C/C++**: You can refer to this [link](https://github.com/wtxfrancise/quantize_models) to run `Chimera` or `Phoenix` on your PC.
## 🏭 Deployment
### Launch a controller
```shell
python -m llmzoo.deploy.webapp.controller
```
### Launch a model worker
```shell
python -m llmzoo.deploy.webapp.model_worker --model-path /path/to/weights/
```
### Launch a gradio web server
```shell
python -m llmzoo.deploy.webapp.gradio_web_server
```
Now, you can open your browser and chat with a model.
## 😀 Training by yourself
### Prepare the data
You can either download the [phoenix-sft-data-v1](https://huggingface.co/datasets/FreedomIntelligence/phoenix-sft-data-v1) data or prepare your own data. Put your data on the path `data/data.json`.
### Training
For `Phoenix`, run
```shell
bash scripts/train_phoenix_7b.sh
```
For `Chimera`, prepare the LLaMA weights following [this instruction](https://huggingface.co/docs/transformers/main/model_doc/llama) and run
```shell
bash scripts/train_chimera_7b.sh
bash scripts/train_chimera_13b.sh
```
## 🤖 Limitations
Our goal in releasing our models is to assist our community in better replicating ChatGPT/GPT4. We are not targeting
competition with other competitors, as benchmarking models is a challenging task. Our models face similar models to
those of ChatGPT/GPT4, which include:
- Lack of common sense: our models may not always have the ability to apply common sense knowledge to situations, which
can lead to nonsensical or inappropriate responses.
- Limited knowledge domain: our models' knowledge is based on the data it was trained on, and it may not have the
ability to provide accurate or relevant responses outside that domain.
- Biases: our models may have biases that reflect the biases in the data it was trained on, which can result in
unintended consequences or unfair treatment.
- Inability to understand emotions: While our models can understand language, it may not always be able to understand
the emotional tone behind it, which can lead to inappropriate or insensitive responses.
- Misunderstandings due to context: our models may misunderstand the context of a conversation, leading to
misinterpretation and incorrect responses.
## 🙌 Contributors
LLM Zoo is mainly contributed by:
- Data and Model: [Zhihong Chen](https://zhjohnchan.github.io/), [Junying Chen](), [Hongbo Zhang](), [Feng Jiang](https://fjiangai.github.io/)
, [Chen Zhang](https://genezc.github.io/), [Benyou Wang](https://wabyking.github.io/old.html) (Advisor)
- Evaluation: [Fei Yu](https://github.com/OakYU), [Tiannan Wang](), [Guiming Chen]()
- Others: Zhiyi Zhang, Jianquan Li and Xiang Wan
As an open-source project, we are open to contributions. Feel free to contribute if you have any ideas or find any
issue.
## Acknowledgement
We are aware that our works are inspired by the following works, including but not limited to
- LLaMA: https://github.com/facebookresearch/llama
- Bloom: https://huggingface.co/bigscience/bloom
- Self-instruct: https://github.com/yizhongw/self-instruct
- Alpaca: https://github.com/tatsu-lab/stanford_alpaca
- Vicuna: https://github.com/lm-sys/FastChat
Without these, nothing could happen in this repository.
## Citation
```angular2
@article{phoenix-2023,
title={Phoenix: Democratizing ChatGPT across Languages},
author={Zhihong Chen and Feng Jiang and Junying Chen and Tiannan Wang and Fei Yu and Guiming Chen and Hongbo Zhang and Juhao Liang and Chen Zhang and Zhiyi Zhang and Jianquan Li and Xiang Wan and Benyou Wang and Haizhou Li},
journal={arXiv preprint arXiv:2304.10453},
year={2023}
}
```
```angular2
@misc{llm-zoo-2023,
title={LLM Zoo: democratizing ChatGPT},
author={Zhihong Chen and Junying Chen and Hongbo Zhang and Feng Jiang and Guiming Chen and Fei Yu and Tiannan Wang and Juhao Liang and Chen Zhang and Zhiyi Zhang and Jianquan Li and Xiang Wan and Haizhou Li and Benyou Wang},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/FreedomIntelligence/LLMZoo}},
}
```
We are from the School of Data Science, the Chinese University of Hong Kong, Shenzhen (CUHKSZ) and the Shenzhen Rsearch
Institute of Big Data (SRIBD).
## Star History
[](https://star-history.com/#FreedomIntelligence/LLMZoo&Date)