cwielenb/CNCF-Stuff
收藏Hugging Face2024-06-07 更新2024-06-12 收录
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资源简介:
---
library_name: peft
base_model: google/gemma-1.1-7b-it
---
# Model Card for Model ID
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## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
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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
- **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]
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## 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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**BibTeX:**
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**APA:**
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## Model Card Authors [optional]
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### Framework versions
- PEFT 0.11.1
提供机构:
cwielenb
原始信息汇总
数据集概述
基本信息
- 库名称: peft
- 基础模型: google/gemma-1.1-7b-it
模型详情
- 开发团队: [待补充]
- 资金来源: [待补充]
- 共享者: [待补充]
- 模型类型: [待补充]
- 支持语言: [待补充]
- 许可证: [待补充]
- 微调自模型: [待补充]
模型资源
- 代码库: [待补充]
- 相关论文: [待补充]
- 演示: [待补充]
使用场景
- 直接使用: [待补充]
- 下游应用: [待补充]
- 超出范围的使用: [待补充]
训练细节
- 训练数据: [待补充]
- 训练过程:
- 预处理: [待补充]
- 训练超参数:
- 训练模式: [待补充]
- 速度、大小、时间: [待补充]
评估
- 测试数据、因素与指标:
- 测试数据: [待补充]
- 评估因素: [待补充]
- 评估指标: [待补充]
- 评估结果: [待补充]
环境影响
- 硬件类型: [待补充]
- 使用时长: [待补充]
- 云服务提供商: [待补充]
- 计算区域: [待补充]
- 碳排放量: [待补充]
技术规格
- 模型架构与目标: [待补充]
- 计算基础设施:
- 硬件: [待补充]
- 软件: [待补充]
引用信息
- BibTeX: [待补充]
- APA格式: [待补充]



