llama3.3_my_lora

# Model Card for Model ID

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## Model Details

### Model Description

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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

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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

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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]
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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Software

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## Citation [optional]

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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 0.12.0

# 模型卡片（针对模型ID）

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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]
- **（自然语言处理领域）适用语言：** [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）

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

## 模型用途

<!-- 解答关于该模型预期使用场景的相关问题，包括可预见的模型使用者及受该模型影响的群体。 -->

### 直接使用

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[需补充更多信息]

### 下游使用（可选）

<!-- 本节说明针对特定任务微调，或接入更大生态/应用后的模型使用场景。 -->

[需补充更多信息]

### 超出适用范围的使用

<!-- 本节说明误用、恶意使用，以及该模型无法良好适配的使用场景。 -->

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## 偏见、风险与局限性

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[需补充更多信息]

### 建议

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无论是直接使用者还是下游使用者，均应知晓该模型存在的风险、偏见与局限性。如需进一步建议，需补充更多信息。

## 模型快速上手指南

可通过以下代码快速上手该模型。

[需补充更多信息]

## 训练详情

### 训练数据

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[需补充更多信息]

### 训练流程

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#### 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] <!--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）

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[需补充更多信息]

## 模型评估

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### 测试数据、影响因素与评估指标

#### 测试数据

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[需补充更多信息]

#### 影响因素

<!-- 指评估时进行拆分的维度，例如子群体或应用领域。 -->

[需补充更多信息]

#### 评估指标

<!-- 指本次评估使用的指标，最好附带选用原因说明。 -->

[需补充更多信息]

### 评估结果

[需补充更多信息]

#### 总结

## 模型可解释性分析（可选）

<!-- 本节收录与模型可解释性相关的研究工作。 -->

[需补充更多信息]

## 环境影响

<!-- 此处应填写总排放量（以克CO₂当量计）及其他相关考量，例如电力使用情况。可按以下示例文本修改。 -->

碳排放量可通过[Lacoste等人(2019)](https://arxiv.org/abs/1910.09700)提出的[机器学习影响计算器（Machine Learning Impact calculator）](https://mlco2.github.io/impact#compute)进行估算。

- **硬件类型：** [需补充更多信息]
- **使用时长：** [需补充更多信息]
- **云服务商：** [需补充更多信息]
- **计算区域：** [需补充更多信息]
- **碳排放总量：** [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

[需补充更多信息]

#### 软件

[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，应在此处提供APA和BibTeX格式的引用信息。 -->

**BibTeX格式：**

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**APA格式：**

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## 术语表（可选）

<!-- 若有需要，可在此处收录有助于读者理解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]

## 模型卡片编写者（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 0.12.0