coffee-20000chkpt

# Model Card for Model ID

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## Model Details

### Model Description

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- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary

## Model Examination [optional]

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## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.11.1

# 模型卡片（Model Card）：模型编号
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## 模型详情
### 模型描述
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- **开发方：** [需补充更多信息]
- **资助方（可选）：** [需补充更多信息]
- **共享方（可选）：** [需补充更多信息]
- **模型类型：** [需补充更多信息]
- **（自然语言处理）所用语言：** [需补充更多信息]
- **许可证：** [需补充更多信息]
- **微调自模型（可选）：** [需补充更多信息]

### 模型来源（可选）
<!-- 请提供该模型的基础链接。 -->

- **代码仓库：** [需补充更多信息]
- **相关论文（可选）：** [需补充更多信息]
- **演示Demo（可选）：** [需补充更多信息]

## 模型用途
<!-- 请阐述该模型的预期使用场景，包括可预见的使用者与受该模型影响的群体。 -->

### 直接使用
<!-- 本小节用于说明无需微调或接入更大生态/应用的模型使用方式。 -->
[需补充更多信息]

### 下游应用（可选）
<!-- 本小节用于说明针对特定任务微调后的模型使用方式，或接入更大生态/应用的场景。 -->
[需补充更多信息]

### 超出适用范围的使用场景
<!-- 本小节阐述误用、恶意使用，以及该模型无法良好适配的使用场景。 -->
[需补充更多信息]

## 偏见、风险与局限性
<!-- 本小节用于说明技术与社会技术层面的局限性。 -->
[需补充更多信息]

### 建议
<!-- 本小节用于给出针对该模型偏见、风险与技术局限性的相关建议。 -->
用户（包括直接使用者与下游应用方）应充分知晓该模型存在的风险、偏见与局限性。如需进一步完善建议，请补充更多信息。

## 模型快速上手指南
使用以下代码即可快速上手该模型。
[需补充更多信息]

## 训练细节
### 训练数据
<!-- 此处应链接至数据集卡片，也可附带一段简短的训练数据概述，以及数据预处理或额外筛选的相关文档。 -->
[需补充更多信息]

### 训练流程
<!-- 本小节与技术规格紧密相关。若内容与训练流程相关，请链接至对应章节。 -->
#### 预处理（可选）
[需补充更多信息]

#### 训练超参数
- **训练模式：** [需补充更多信息] <!-- 例如：fp32、fp16混合精度、bf16混合精度、bf16非混合精度、fp16非混合精度、fp8混合精度 -->

#### 速度、规模与耗时（可选）
<!-- 本小节提供吞吐量、起止时间、相关检查点大小等信息。 -->
[需补充更多信息]

## 模型评估
<!-- 本小节描述评估协议并展示评估结果。 -->
### 测试数据、细分维度与评估指标
#### 测试数据
<!-- 若可行，请链接至数据集卡片。 -->
[需补充更多信息]

#### 细分维度
<!-- 此处为评估时的细分维度，例如子群体或应用领域。 -->
[需补充更多信息]

#### 评估指标
<!-- 此处为所用的评估指标，理想情况下应附带选择该指标的原因说明。 -->
[需补充更多信息]

### 评估结果
[需补充更多信息]

#### 评估总结

## 模型可解释性分析（可选）
<!-- 此处收录与模型可解释性相关的研究内容。 -->
[需补充更多信息]

## 环境影响
<!-- 此处应填写总碳排放量（以CO₂当量克数为单位）及其他相关考量，例如电力消耗。请编辑以下示例文本： -->
可通过[机器学习影响计算器（Machine Learning Impact calculator）](https://mlco2.github.io/impact#compute)估算碳排放量，该工具由Lacoste等人（2019）在[论文](https://arxiv.org/abs/1910.09700)中提出。

- **硬件类型：** [需补充更多信息]
- **训练时长：** [需补充更多信息]
- **云服务提供商：** [需补充更多信息]
- **计算区域：** [需补充更多信息]
- **碳排放量：** [需补充更多信息]

## 技术规格（可选）
### 模型架构与训练目标
[需补充更多信息]

### 计算基础设施
[需补充更多信息]
#### 硬件配置
[需补充更多信息]
#### 软件环境
[需补充更多信息]

## 引用信息（可选）
<!-- 若存在介绍该模型的论文或博客文章，请在此处提供其APA与BibTeX格式的引用信息。 -->
**BibTeX格式引用：**
[需补充更多信息]

**APA格式引用：**
[需补充更多信息]

## 术语表（可选）
<!-- 若有需要，请在此处收录有助于读者理解该模型或模型卡片的术语与计算公式。 -->
[需补充更多信息]

## 更多信息（可选）
[需补充更多信息]

## 模型卡片作者（可选）
[需补充更多信息]

## 模型卡片联系方式
[需补充更多信息]

### 框架版本
- PEFT 0.11.1