---
base_model: Qwen/Qwen2.5-7B-Instruct
datasets:
- u-10bei/sft_alfworld_trajectory_dataset_v5
- ShogoMu/dbbench_u-10bei_sft_dataset_modified_v2
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- agent
- tool-use
- alfworld
- dbbench
---

# qwen25_7b_lora_agentbench_v6_e4

This repository provides a **merged model** fine-tuned from
**Qwen/Qwen2.5-7B-Instruct**. The fine-tuning was performed using **LoRA + Unsloth** and the resulting adapter has been merged back into the base model weights.

This repository contains **full model weights**, making it ready for inference
without the need to load a separate adapter.

## Training Objective

This model is optimized for **multi-turn agent tasks**, specifically for
ALFWorld (household navigation/interaction) and DBBench (database operations).

The training process applied loss to **all assistant turns** in the multi-turn
trajectories, allowing the model to learn not just final answers, but also
intermediate reasoning (Thought), environment observation processing,
action selection, and error recovery.

## Training Configuration

- **Base model:** Qwen/Qwen2.5-7B-Instruct
- **Method:** LoRA (merged post-training)
- **Max sequence length:** 2048
- **Epochs:** 4
- **Learning rate:** 2e-06
- **LoRA Parameters:** r=64, alpha=128

## Usage

This model can be loaded using the standard `transformers` library or
deployed with `vLLM` (recommended for evaluation).

### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your_hf_id/your_repo_name"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
