---
base_model: melon1891/agentbench-qwen3-4b-lr5e6-20260224v2
datasets:
- melon1891/reasoning-chain-distilled-317
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- agent
- tool-use
- alfworld
- dbbench
---

# agentbench-qwen3-4b-2stage-reasoning-20260228

A full model fine-tuned from **melon1891/agentbench-qwen3-4b-lr5e6-20260224v2** using LoRA + Unsloth, with the adapter merged into the base model.

## Training Objective

This model 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: melon1891/agentbench-qwen3-4b-lr5e6-20260224v2
- Method: LoRA (merged into base)
- Max sequence length: 8192
- Epochs: 3
- Learning rate: 1e-06
- LoRA: r=16, alpha=32

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("melon1891/agentbench-qwen3-4b-2stage-reasoning-20260228")
tokenizer = AutoTokenizer.from_pretrained("melon1891/agentbench-qwen3-4b-2stage-reasoning-20260228")
```

## Sources & Terms (IMPORTANT)

Training data: melon1891/reasoning-chain-distilled-317

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.