new_50000steps
收藏魔搭社区2025-11-26 更新2025-11-03 收录
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# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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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
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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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]
- **Compute Region:** [More Information Needed]
- **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]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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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) 针对 模型ID(Model ID)
<!-- 简要概述该模型的功能与定位。 -->
## 模型详情(Model Details)
### 模型描述(Model Description)
<!-- 详细阐述该模型的具体内涵。 -->
- **开发者:** [需补充更多信息]
- **资助方(可选):** [需补充更多信息]
- **分享方(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(NLP)领域适用语言:** [需补充更多信息]
- **授权协议:** [需补充更多信息]
- **微调来源模型(可选):** [需补充更多信息]
### 模型来源(可选)(Model Sources [optional])
<!-- 提供该模型的基础关联链接。 -->
- **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]
## 用途(Uses)
<!-- 阐述该模型的预设使用场景,涵盖可预见的使用主体与受其影响的相关群体。 -->
### 直接使用(Direct Use)
<!-- 本节介绍无需微调或接入更大生态系统/应用即可直接使用的模型场景。 -->
[需补充更多信息]
### 下游使用(可选)(Downstream Use [optional])
<!-- 本节介绍针对特定任务完成微调,或接入更大生态系统/应用后的模型使用场景。 -->
[需补充更多信息]
### 超范围使用(Out-of-Scope Use)
<!-- 本节说明误用、恶意使用,以及该模型难以良好适配的各类使用场景。 -->
[需补充更多信息]
## 偏见、风险与局限性(Bias, Risks, and Limitations)
<!-- 本节旨在阐述技术层面与社会技术层面的各类局限性。 -->
[需补充更多信息]
### 建议(Recommendations)
<!-- 本节旨在针对模型的偏见、风险与技术局限性提供针对性建议。 -->
所有使用者(包括直接使用者与下游使用者)均应充分知晓该模型存在的风险、偏见与局限性,相关进一步建议仍需补充更多信息。
## 快速上手该模型(How to Get Started with the Model)
使用以下代码即可快速上手该模型。
[需补充更多信息]
## 训练详情(Training Details)
### 训练数据(Training Data)
<!-- 本节应关联至数据集卡片,可附带简短的训练数据核心概述,以及数据预处理、额外筛选相关的文档说明。 -->
[需补充更多信息]
### 训练流程(Training Procedure)
<!-- 本节与技术规格章节关联紧密,若相关内容与训练流程相关,应跳转至对应章节进行查阅。 -->
#### 预处理(可选)(Preprocessing [optional])
[需补充更多信息]
#### 训练超参数(Training Hyperparameters)
- **训练策略:** [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)(Speeds, Sizes, Times [optional])
<!-- 本节提供吞吐量、训练起止时间、相关检查点(checkpoint)规模等相关信息。 -->
[需补充更多信息]
## 评估(Evaluation)
<!-- 本节介绍评估协议并展示对应的评估结果。 -->
### 测试数据、影响因素与评估指标(Testing Data, Factors & Metrics)
#### 测试数据(Testing Data)
<!-- 若条件允许,此处应关联至数据集卡片。 -->
[需补充更多信息]
#### 影响因素(Factors)
<!-- 此处为评估时的拆解维度,例如细分用户群体或应用领域。 -->
[需补充更多信息]
#### 评估指标(Metrics)
<!-- 此处为所采用的评估指标,理想情况下应附带对应的选用依据说明。 -->
[需补充更多信息]
### 评估结果(Results)
[需补充更多信息]
#### 总结(Summary)
## 模型可解释性分析(可选)(Model Examination [optional])
<!-- 本节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(Environmental Impact)
<!-- 此处应填写总碳排放量(以克二氧化碳当量计)与其他相关考量因素,例如电力消耗。请根据实际情况编辑下方示例文本。 -->
碳排放量可通过[机器学习影响计算器(Machine Learning Impact calculator)](https://mlco2.github.io/impact#compute)进行估算,该工具出自[Lacoste等人(2019)](https://arxiv.org/abs/1910.09700)的研究。
- **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)(Technical Specifications [optional])
### 模型架构与训练目标(Model Architecture and Objective)
[需补充更多信息]
### 计算基础设施(Compute Infrastructure)
[需补充更多信息]
#### 硬件(Hardware)
[需补充更多信息]
#### 软件(Software)
[需补充更多信息]
## 引用(可选)(Citation [optional])
<!-- 若有介绍该模型的论文或博客文章,此处应收录其APA与Bibtex格式的引用信息。 -->
**BibTeX格式:**
[需补充更多信息]
**APA格式:**
[需补充更多信息]
## 术语表(可选)(Glossary [optional])
<!-- 若有需要,本节可收录有助于读者理解该模型或模型卡片的相关术语与计算公式。 -->
[需补充更多信息]
## 更多信息(可选)(More Information [optional])
[需补充更多信息]
## 模型卡片作者(可选)(Model Card Authors [optional])
[需补充更多信息]
## 模型卡片联系方式(Model Card Contact)
[需补充更多信息]
### 框架版本
- 参数高效微调(Parameter-Efficient Fine-Tuning,PEFT)0.11.1
提供机构:
maas
创建时间:
2025-11-02



