Model Support

Supported models

• meta-llama/Llama-2-7b-chat-hf
  • example: python3 -m fastchat.serve.cli --model-path meta-llama/Llama-2-7b-chat-hf
• Vicuna, Alpaca, LLaMA, Koala
  • example: python3 -m fastchat.serve.cli --model-path lmsys/vicuna-7b-v1.3
• BAAI/AquilaChat-7B
• BAAI/bge-large-en
• baichuan-inc/baichuan-7B
• BlinkDL/RWKV-4-Raven
  • example: python3 -m fastchat.serve.cli --model-path ~/model_weights/RWKV-4-Raven-7B-v11x-Eng99%-Other1%-20230429-ctx8192.pth
• bofenghuang/vigogne-2-7b-instruct
• bofenghuang/vigogne-2-7b-chat
• camel-ai/CAMEL-13B-Combined-Data
• codellama/CodeLlama-7b-Instruct-hf
• databricks/dolly-v2-12b
• FlagAlpha/Llama2-Chinese-13b-Chat
• FreedomIntelligence/phoenix-inst-chat-7b
• FreedomIntelligence/ReaLM-7b-v1
• h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b
• internlm/internlm-chat-7b
• lcw99/polyglot-ko-12.8b-chang-instruct-chat
• lmsys/fastchat-t5-3b-v1.0
• mosaicml/mpt-7b-chat
  • example: python3 -m fastchat.serve.cli --model-path mosaicml/mpt-7b-chat
• Neutralzz/BiLLa-7B-SFT
• nomic-ai/gpt4all-13b-snoozy
• NousResearch/Nous-Hermes-13b
• openaccess-ai-collective/manticore-13b-chat-pyg
• OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5
• VMware/open-llama-7b-v2-open-instruct
• Phind/Phind-CodeLlama-34B-v2
• project-baize/baize-v2-7b
• Qwen/Qwen-7B-Chat
• Salesforce/codet5p-6b
• StabilityAI/stablelm-tuned-alpha-7b
• THUDM/chatglm-6b
• THUDM/chatglm2-6b
• tiiuae/falcon-40b
• tiiuae/falcon-180B-chat
• timdettmers/guanaco-33b-merged
• togethercomputer/RedPajama-INCITE-7B-Chat
• WizardLM/WizardLM-13B-V1.0
• WizardLM/WizardCoder-15B-V1.0
• HuggingFaceH4/starchat-beta
• Any EleutherAI pythia model such as pythia-6.9b
• Any Peft adapter trained on top of a model above. To activate, must have peft in the model path. Note: If loading multiple peft models, you can have them share the base model weights by setting the environment variable PEFT_SHARE_BASE_WEIGHTS=true in any model worker.

How to support a new model

To support a new model in FastChat, you need to correctly handle its prompt template and model loading. The goal is to make the following command run with the correct prompts.

python3 -m fastchat.serve.cli --model [YOUR_MODEL_PATH]

You can run this example command to learn the code logic.

python3 -m fastchat.serve.cli --model lmsys/vicuna-7b-v1.3

You can add --debug to see the actual prompt sent to the model.

Steps

FastChat uses the Conversation class to handle prompt templates and BaseModelAdapter class to handle model loading.

1. Implement a conversation template for the new model at fastchat/conversation.py. You can follow existing examples and use register_conv_template to add a new one. Please also add a link to the official reference code if possible.
2. Implement a model adapter for the new model at fastchat/model/model_adapter.py. You can follow existing examples and use register_model_adapter to add a new one.
3. (Optional) add the model name to the “Supported models” section above and add more information in fastchat/model/model_registry.py.

After these steps, the new model should be compatible with most FastChat features, such as CLI, web UI, model worker, and OpenAI-compatible API server. Please do some testing with these features as well.