README.md

---
base_model: Qwen/Qwen2.5-7B-Instruct
datasets:
- SFT_DATASET_DB
- SFT_DATASET_ALF
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- agent
- tool-use
- alfworld
- dbbench
- synthetic-data
- sql
---

# qwen2.5-7b-agent-trajectory-lora

This repository provides a **LoRA adapter** fine-tuned from
**Qwen/Qwen2.5-7B-Instruct** 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).
Additional self-generated synthetic datasets are included to target known
failure modes (e.g., SQL MAX aggregation and action efficiency).

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/Qwen2.5-7B-Instruct
- Method: LoRA (full precision base)
- Max sequence length: 2048
- Epochs: 8
- Learning rate: 1e-06
- LoRA: r=64, alpha=128

## Usage

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

base = "Qwen/Qwen2.5-7B-Instruct"
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 (organizer-provided): SFT_DATASET_DB , SFT_DATASET_ALF
Additional training data (self-generated): synthetic_action_sft.jsonl , synthetic_sql_sft.jsonl

Dataset License (organizer-provided datasets): MIT License.
These organizer-provided datasets are used under the terms of the MIT License (including the copyright notice).

Synthetic data terms: The synthetic JSONL files are generated by the participant for this competition.
Their use and redistribution (if any) follow the competition rules and applicable policies.

Compliance: Users must comply with the MIT License for organizer-provided datasets and the base model’s original terms of use.