---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- mark-22/alfworld_cleaned_for_agentbench
- mark-22/dbbench_cleaned_for_agentbench
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- agent
- multi-task
- alfworld
- dbbench
- unsloth
---

# Qwen3-4B Dual-Skill Agent (ALFWorld & DBBench) LoRA

This repository provides a **Dual-Skill LoRA adapter** fine-tuned from **Qwen/Qwen3-4B-Instruct-2507**.
It is specifically optimized for two distinct agentic tasks: **Household operations (ALFWorld)** and **Database interactions (DBBench)**.

## Key Improvements & Features

- **Multi-task Generalization**: Balanced training on both ALFWorld and DBBench, allowing the model to switch contexts based on the system prompt.
- **Optimized Trajectories**: All training data was pre-cleaned to a maximum of **3072 tokens** to ensure high-density learning without truncation of critical terminal actions.
- **Assistant-Only Loss**: Fine-tuned using a specialized collator that applies loss only to assistant turns (THOUGHT/ACTION), preventing the model from memorizing environment descriptions.
- **Robustness**: Includes error-recovery trajectories where the agent learns to correct its path after receiving "Nothing happened" or SQL errors from the environment.
- **Cleaner Reasoning**: Removed potential "tools" format traps to align strictly with the AgentBench evaluation parser.
- **Data Shuffling**: Integrated a mechanism to completely shuffle ALFWorld and DBBench data, eliminating bias during training and preventing catastrophic forgetting.

## Training Configuration

| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen3-4B-Instruct-2507 |
| Hardware | NVIDIA A100 SXM4 40GB |
| Precision | bfloat16 |
| Max context length | 3072 tokens |
| Epochs | 2 |
| Learning rate | 2e-06 |
| Batch size (effective) | 8 |
| LoRA Rank / Alpha | r=64 / a=128 |
| Target Modules | All Linear Layers (Q,K,V,O,Gate,Up,Down) |

## Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "your_id/your-repo-name"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)
```

## Training Data Sources

1. **ALFWorld Cleaned**: `mark-22/alfworld_cleaned_for_agentbench` - Focused on household task completion and navigation.
2. **DBBench Cleaned**: `mark-22/dbbench_cleaned_for_agentbench` - Focused on SQL generation and database manipulation (UPDATE/SELECT).

## License

This adapter is distributed under the Apache-2.0 license. Please ensure compliance with the base model's usage terms.