OpenChat: Less is More for Open-source Models
OpenChat is a series of open-source language models fine-tuned on a diverse and high-quality dataset of multi-round conversations. With only ~6K GPT-4 conversations filtered from the ~90K ShareGPT conversations, OpenChat is designed to achieve high performance with limited data.
Generic models:
- OpenChat: based on LLaMA-13B (2048 context length)
- π 105.7% of ChatGPT score on Vicuna GPT-4 evaluation
- π₯ 80.9% Win-rate on AlpacaEval
- π€ Only used 6K data for finetuning!!!
- OpenChat-8192: based on LLaMA-13B (extended to 8192 context length)
- 106.6% of ChatGPT score on Vicuna GPT-4 evaluation
- 79.5% of ChatGPT score on Vicuna GPT-4 evaluation
Code models:
- OpenCoderPlus: based on StarCoderPlus (native 8192 context length)
- 102.5% of ChatGPT score on Vicuna GPT-4 evaluation
- 78.7% Win-rate on AlpacaEval
Note: Please load the pretrained models using bfloat16
Code and Inference Server
We provide the full source code, including an inference server compatible with the "ChatCompletions" API, in the OpenChat GitHub repository.
Web UI
OpenChat also includes a web UI for a better user experience. See the GitHub repository for instructions.
Conversation Template
The conversation template involves concatenating tokens.
Besides base model vocabulary, an end-of-turn token <|end_of_turn|>
is added, with id eot_token_id
.
# OpenChat
[bos_token_id] + tokenize("Human: ") + tokenize(user_question) + [eot_token_id] + tokenize("Assistant: ")
# OpenCoder
tokenize("User:") + tokenize(user_question) + [eot_token_id] + tokenize("Assistant:")
Hint: In BPE, tokenize(A) + tokenize(B)
does not always equals to tokenize(A + B)
Following is the code for generating the conversation templates:
@dataclass
class ModelConfig:
# Prompt
system: Optional[str]
role_prefix: dict
ai_role: str
eot_token: str
bos_token: Optional[str] = None
# Get template
def generate_conversation_template(self, tokenize_fn, tokenize_special_fn, message_list):
tokens = []
masks = []
# begin of sentence (bos)
if self.bos_token:
t = tokenize_special_fn(self.bos_token)
tokens.append(t)
masks.append(False)
# System
if self.system:
t = tokenize_fn(self.system) + [tokenize_special_fn(self.eot_token)]
tokens.extend(t)
masks.extend([False] * len(t))
# Messages
for idx, message in enumerate(message_list):
# Prefix
t = tokenize_fn(self.role_prefix[message["from"]])
tokens.extend(t)
masks.extend([False] * len(t))
# Message
if "value" in message:
t = tokenize_fn(message["value"]) + [tokenize_special_fn(self.eot_token)]
tokens.extend(t)
masks.extend([message["from"] == self.ai_role] * len(t))
else:
assert idx == len(message_list) - 1, "Empty message for completion must be on the last."
return tokens, masks
MODEL_CONFIG_MAP = {
# OpenChat / OpenChat-8192
"openchat": ModelConfig(
# Prompt
system=None,
role_prefix={
"human": "Human: ",
"gpt": "Assistant: "
},
ai_role="gpt",
eot_token="<|end_of_turn|>",
bos_token="<s>",
),
# OpenCoder / OpenCoderPlus
"opencoder": ModelConfig(
# Prompt
system=None,
role_prefix={
"human": "User:",
"gpt": "Assistant:"
},
ai_role="gpt",
eot_token="<|end_of_turn|>",
bos_token=None,
)
}
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