LLM Zoo: democratizing ChatGPT

⚡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.⚡ [Tech Report]

✨ Latest News

• [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.

📚 Data

Instruction data

• Multilingual instructions (language-agostic instructions with post-translation)

+ 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

+ (Role, Instruction, Input) seeds
- ---(Step 1) Self Instruct--->
+ (Role, Instruction, Input) samples
- ---(Step 2) generate output Instruct--->
+ (Role, Instruction, Input) ---> Output

Check open intruction dataset in InstructionZoo.

GPT-API-Accelerate Tool may help build ChatGPT-generated data more quickly.

Conversation data

• User-shared conversations

+ ChatGPT conversations shared on the Internet
- ---(Step 1) Crawl--->
+ Multi-round conversation data

Check open User-chatGPT conversation data in OpenChatGPT.

🐼 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 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 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
Phoenix-inst-chat-7b	BLOOMZ-7b1-mt	Instruction + Conversation	parameters

Chimera (LLM mainly for Latin and Cyrillic languages)

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, 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)
Chimera-chat-13b	LLaMA-13b	Conversation	parameters (delta)
Chimera-inst-chat-7b	LLaMA-7b	Instruction + Conversation	parameters (delta)
Chimera-inst-chat-13b	LLaMA-13b	Instruction + Conversation	parameters (delta)

Due to LLaMA's license restrictions, we follow FastChat to release our delta weights. To use Chimera, download the original LLaMA weights and run the script:

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: Its Chinese name is 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 delopy it in public cloud servers in case of massive requests.

See our models in https://www.huatuogpt.cn/ (API key required) . Similar biomedical models could be seen in biomedical LLMs

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. Baidu-Wenxin	96.8%
Phoenix-inst-chat-7b vs. ChatGLM-6b	94.6%
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. Baidu-Wenxin	29	25	46
Phoenix vs. ChatGLM-6b	36	11	53
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

Model	Ratio
Chimera-chat-7b vs. ChatGPT	85.2%
Chimera-chat-13b vs. ChatGPT	92.6%
Chimera-inst-chat-13b vs. ChatGPT	96.6%

🏭 Deployment

Install

Run the following command to install the required packages:

pip install -r requirements.txt

CLI Inference

python -m llmzoo.deploy.cli --model-name /path/to/weights/

For example, for phoenix, run

python -m llmzoo.deploy.cli --model-name FreedomIntelligence/phoenix-inst-chat-7b

and it will download the model from huggingface automatically. For Chimera, please follow this to prepare the weights.

🤖 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 of 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, Junying Chen, Hongbo Zhang, Feng Jiang , Chen Zhang, Benyou Wang (Advisor)
• Evaluation: Fei Yu, 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

@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}
}

@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).