hle_all_lora
收藏魔搭社区2025-06-09 更新2025-03-29 收录
下载链接:
https://modelscope.cn/datasets/TobyYang7/hle_all_lora
下载链接
链接失效反馈官方服务:
资源简介:
# Model Card for Model ID
## Model Details
### Model Description
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
### Direct Use
[More Information Needed]
### Downstream Use [optional]
[More Information Needed]
### Out-of-Scope Use
[More Information Needed]
## Bias, Risks, and Limitations
[More Information Needed]
### Recommendations
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.
[More Information Needed]
## Training Details
### Training Data
[More Information Needed]
### Training Procedure
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed]
#### Speeds, Sizes, Times [optional]
[More Information Needed]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
[More Information Needed]
#### Factors
[More Information Needed]
#### Metrics
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
[More Information Needed]
## Environmental Impact
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]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.12.0
# 模型ID对应模型卡片
## 模型详情
### 模型描述
- **开发者:** [待补充更多信息]
- **资助方(可选):** [待补充更多信息]
- **分享方(可选):** [待补充更多信息]
- **模型类型:** [待补充更多信息]
- **自然语言处理支持语言:** [待补充更多信息]
- **授权协议:** [待补充更多信息]
- **基于以下模型微调(可选):** [待补充更多信息]
### 模型来源(可选)
- **代码仓库:** [待补充更多信息]
- **相关论文(可选):** [待补充更多信息]
- **演示Demo(可选):** [待补充更多信息]
## 模型用途
### 直接使用
[待补充更多信息]
### 下游应用(可选)
[待补充更多信息]
### 不适用场景
[待补充更多信息]
## 偏差、风险与局限性
[待补充更多信息]
### 建议
无论是直接使用者还是下游应用开发者,均需知晓该模型存在的风险、偏差与局限性。如需获取进一步的指导建议,仍需补充更多相关信息。
## 如何快速上手该模型
可通过以下代码快速启动该模型。
[待补充更多信息]
## 训练详情
### 训练数据
[待补充更多信息]
### 训练流程
#### 预处理(可选)
[待补充更多信息]
#### 训练超参数
- **训练范式:** [待补充更多信息]
#### 训练速度、规模与时长(可选)
[待补充更多信息]
## 模型评估
### 测试数据、影响因素与评估指标
#### 测试数据
[待补充更多信息]
#### 影响因素
[待补充更多信息]
#### 评估指标
[待补充更多信息]
### 评估结果
[待补充更多信息]
#### 结果总结
## 模型检验(可选)
[待补充更多信息]
## 环境影响
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](https://mlco2.github.io/impact#compute)估算碳排放情况。
- **硬件类型:** [待补充更多信息]
- **使用时长:** [待补充更多信息]
- **云服务提供商:** [待补充更多信息]
- **计算区域:** [待补充更多信息]
- **碳排放量:** [待补充更多信息]
## 技术规格(可选)
### 模型架构与训练目标
[待补充更多信息]
### 计算基础设施
[待补充更多信息]
#### 硬件
[待补充更多信息]
#### 软件
[待补充更多信息]
## 引用(可选)
**BibTeX格式:**
[待补充更多信息]
**APA格式:**
[待补充更多信息]
## 术语表(可选)
[待补充更多信息]
## 更多信息(可选)
[待补充更多信息]
## 模型卡片作者(可选)
[待补充更多信息]
## 模型卡片联系方式
[待补充更多信息]
### 框架版本
- 参数高效微调(Parameter-Efficient Fine-Tuning,PEFT) 0.12.0
提供机构:
maas
创建时间:
2025-03-25



