---
language:
- en
tags:
- llama
---

# 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)
   - **🚀 105.7%** of ChatGPT score on Vicuna GPT-4 evaluation
   - **🔥 80.9%** Win-rate on AlpacaEval
   - **🤗 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](https://github.com/imoneoi/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`.

```python
# 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:

```python
@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,
    )
}
```
# [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_openchat__opencoderplus)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 43.17   |
| ARC (25-shot)         | 50.6          |
| HellaSwag (10-shot)   | 78.22    |
| MMLU (5-shot)         | 42.73         |
| TruthfulQA (0-shot)   | 50.72   |
| Winogrande (5-shot)   | 66.14   |
| GSM8K (5-shot)        | 4.62        |
| DROP (3-shot)         | 9.14         |