69 lines
1.7 KiB
Markdown
69 lines
1.7 KiB
Markdown
---
|
||
base_model: Qwen/Qwen3-4B-Instruct-2507
|
||
datasets:
|
||
- u-10bei/dbbench_sft_dataset_react
|
||
language:
|
||
- en
|
||
license: apache-2.0
|
||
library_name: peft
|
||
pipeline_tag: text-generation
|
||
tags:
|
||
- lora
|
||
- agent
|
||
- tool-use
|
||
- alfworld
|
||
- dbbench
|
||
---
|
||
|
||
# ＜【課題】Qwen3-4B-LoRA-SFT-DBBench＞
|
||
|
||
This repository provides a **LoRA adapter** fine-tuned from
|
||
**Qwen/Qwen3-4B-Instruct-2507** using **LoRA + Unsloth**.
|
||
|
||
This repository contains **LoRA adapter weights only**.
|
||
The base model must be loaded separately.
|
||
|
||
## Training Objective
|
||
|
||
This adapter is trained to improve **multi-turn agent task performance**
|
||
on ALFWorld (household tasks) and DBBench (database operations).
|
||
|
||
Loss is applied to **all assistant turns** in the multi-turn trajectory,
|
||
enabling the model to learn environment observation, action selection,
|
||
tool use, and recovery from errors.
|
||
|
||
## Training Configuration
|
||
|
||
- Base model: Qwen/Qwen3-4B-Instruct-2507
|
||
- Method: LoRA (full precision base)
|
||
- Max sequence length: 2048
|
||
- Epochs: 1
|
||
- Learning rate: 2e-06
|
||
- LoRA: r=64, alpha=128
|
||
|
||
## Usage
|
||
|
||
```python
|
||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||
from peft import PeftModel
|
||
import torch
|
||
|
||
base = "Qwen/Qwen3-4B-Instruct-2507"
|
||
adapter = "your_id/your-repo"
|
||
|
||
tokenizer = AutoTokenizer.from_pretrained(base)
|
||
model = AutoModelForCausalLM.from_pretrained(
|
||
base,
|
||
torch_dtype=torch.float16,
|
||
device_map="auto",
|
||
)
|
||
model = PeftModel.from_pretrained(model, adapter)
|
||
```
|
||
|
||
## Sources & Terms (IMPORTANT)
|
||
|
||
Training data: u-10bei/dbbench_sft_dataset_react
|
||
|
||
Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
|
||
Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
|