---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/dbbench_sft_dataset_react_v4
- u-10bei/sft_alfworld_trajectory_dataset_v5
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- peft
- unsloth
- agent
- tool-use
- agentbench
- alfworld
- dbbench
---

# qwen3-4b-agentbench-dbalf-lora

This repository provides a **LoRA adapter** fine-tuned from **Qwen/Qwen3-4B-Instruct-2507**
using **LoRA + Unsloth** for **AgentBench-style multi-turn agent trajectories**.

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:

- **DBBench** (database operation / SQL generation trajectories)
- **ALFWorld** (household task trajectories)

Loss is applied to **all assistant turns** in the trajectory, enabling the model to learn:

- environment observation
- action selection
- tool use / operation formatting
- recovery from intermediate errors

## Training Data

- DBBench dataset: `u-10bei/dbbench_sft_dataset_react_v4`
- ALFWorld dataset: `u-10bei/sft_alfworld_trajectory_dataset_v5`
- Mixing ratio (pre-merge target): **DB:ALF = 1:0**

### DB Oversampling (category-aware)
Enabled: **False**

DB category weights used during training-data preparation:

- counting: 1
- comparison: 1
- ranking: 1
- select: 1
- insert: 1
- update: 1
- other: 1

## Training Configuration

- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: LoRA (full precision base)
- Max sequence length: 2048
- Epochs: 1.2
- Learning rate: 3e-06
- LoRA: r=32, alpha=64, dropout=0.0
- Per-device train batch size: 2
- Gradient accumulation: 4

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "AF0815/agentbench"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)
```

## Notes

- This repository is intended for **adapter-only** distribution.
- Please ensure compliance with the **base model license/terms** in addition to this repository's license.
- If you publish evaluation results, it is recommended to report:
  - AgentBench task split / seeds
  - DBBench / ALFWorld mix ratio
  - DB oversampling settings
  - decoding settings

## Sources & Terms (IMPORTANT)

Training data:
- u-10bei/dbbench_sft_dataset_react_v4
- u-10bei/sft_alfworld_trajectory_dataset_v5

Dataset license / terms:
- Please follow the original license and terms of each dataset repository.
- This adapter repository license (**apache-2.0**) applies to the adapter files in this repository.