src/about.py

from dataclasses import dataclass
from enum import Enum
from pathlib import Path
@dataclass
class Task:
    benchmark: str
    metric: str
    col_name: str

REPORT_MD_PATH = Path(__file__).parent.parent / "Files" / "report.md"
with open(REPORT_MD_PATH, "r", encoding="utf-8") as f:
    REPORT_TEXT = f.read()

TITLE = "# LLM Benchmark Leaderboard"

# 替换LLM_BENCHMARKS_TEXT为report.md内容
LLM_BENCHMARKS_TEXT = REPORT_TEXT

CITATION_BUTTON_LABEL = "📖 Citation"
CITATION_BUTTON_TEXT = """If you use this benchmark, please cite: ...
(原citation内容保留)"""

EVALUATION_QUEUE_TEXT = "Models submitted for evaluation will appear here."
# Select your tasks here
# ---------------------------------------------------
class Tasks(Enum):
    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
    task0 = Task("anli_r1", "acc", "ANLI")
    task1 = Task("logiqa", "acc_norm", "LogiQA")

NUM_FEWSHOT = 0 # Change with your few shot
# ---------------------------------------------------



# Your leaderboard name
TITLE = """<h1 align="center" id="space-title">Demo leaderboard</h1>"""

# What does your leaderboard evaluate?
INTRODUCTION_TEXT = """
Intro text
"""

# Which evaluations are you running? how can people reproduce what you have?
LLM_BENCHMARKS_TEXT = f"""
## How it works

## Reproducibility
To reproduce our results, here is the commands you can run:

"""

EVALUATION_QUEUE_TEXT = """
## Some good practices before submitting a model

### 1) Make sure you can load your model and tokenizer using AutoClasses:
```python
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("your model name", revision=revision)
model = AutoModel.from_pretrained("your model name", revision=revision)
tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
```
If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.

Note: make sure your model is public!
Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!

### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!

### 3) Make sure your model has an open license!
This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗

### 4) Fill up your model card
When we add extra information about models to the leaderboard, it will be automatically taken from the model card

## In case of model failure
If your model is displayed in the `FAILED` category, its execution stopped.
Make sure you have followed the above steps first.
If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
"""

CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
CITATION_BUTTON_TEXT = r"""
"""
# Report

## 1. 模型及类别选择

本次实验选用了三类大模型：Llama 3, Mistral 7B, ChatGPT。

- **Llama 3**：开源社区广泛使用，适合中英文任务。
- **Mistral 7B**：轻量级，适合边缘设备。
- **ChatGPT**：闭源，适合通用对话任务，表现最优。

| 模型名称   | 参数量 | 开源情况 | 主要用途         |
|------------|--------|---------|----------------|
| Llama 3    | 70B    | 是      | 多语言任务       |
| Mistral 7B | 7B     | 是      | 低功耗推理任务   |
| ChatGPT    | 未公开 | 否      | 通用对话、推理任务 |

**选择理由**：
- Llama 3和Mistral为开源，方便定制与修改；
- ChatGPT性能优越，作为基准。

---

## 2. 系统实现细节

### Gradio交互界面截图
![Gradio界面](./interface.png)

### 输入与输出流程图
```mermaid
graph TD
    A[用户输入] --> B[Gradio界面]
    B --> C[模型推理]
    C --> D[返回结果]