---
library_name: peft
base_model: H:\Models\Qwen1.5-1.8B-Chat
---
# Model Card for Model ID
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## Model Details
### Model Description
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## Uses
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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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## Environmental Impact
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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).
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### Framework versions
- PEFT 0.11.1
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温馨提示
"llama-factory"是一个数据微调的工具或平台,主要与LoRa(Long Range)技术相关,这是一项低功耗广域网通信技术,常用于物联网(IoT)应用。在给定的文件列表中,我们可以看到以下几个关键文件: 1. **dataset_info.json**:这是一个JSON格式的文件,通常用于存储数据集的相关元信息。这可能包括数据集的描述、大小、样本数量、特征列表、类别信息等。在微调过程中,理解数据集的内容和结构至关重要,因为这将影响模型的训练和性能。 2. **Client.py**:这个文件可能是Python脚本,提供了与"llama-factory"交互的客户端接口。它可能包含了数据加载、模型配置、训练循环、模型评估等功能。通过这个客户端,用户可以方便地定制和运行自己的数据微调任务,尤其是针对LoRa信号处理或数据分析的场景。 3. **checkpoint-2000**:这是一个检查点文件,通常在深度学习模型训练过程中保存。当模型达到特定迭代次数(这里是2000次)时,它的参数会被保存下来,以便后续恢复训练或者进行模型验证。这对于防止训练过程中的中断,以及避免从头
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微调及接口优化.rar (19个子文件)
checkpoint-2000
optimizer.pt 12.08MB
training_args.bin 5KB
trainer_state.json 66KB
added_tokens.json 85B
adapter_config.json 679B
scheduler.pt 1KB
merges.txt 1.59MB
adapter_model.safetensors 6.01MB
vocab.json 2.65MB
tokenizer.json 6.7MB
rng_state.pth 14KB
tokenizer_config.json 2KB
special_tokens_map.json 387B
README.md 5KB
Client.py 446B
customize
multiturn_chat_0.8M.json 944.17MB
multiturn_chat_0.8M_openai.json 1.43MB
instruction2openai.py 1KB
dataset_info.json 12KB
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