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base_model: Qwen/Qwen2.5-7B-Instruct
datasets:
- u-10bei/sft_alfworld_trajectory_dataset_v5
- u-10bei/dbbench_sft_dataset_react_v4
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- agent
- tool-use
- alfworld
- dbbench
---
# qwen2.5-7b-alf-dbb-merged-final
This repository provides a **merged full model** based on
**Qwen/Qwen2.5-7B-Instruct**.
## Model Construction Pipeline
1. Train LoRA adapter on ALFWorld
2. Train LoRA adapter on DBBench
3. Merge adapters using `ties` (density=0.1)
4. Apply additional stabilization fine-tuning (LoRA)
5. Merge final adapter into base model
This repository contains **full merged weights (no adapter required)**.
## Final Training Configuration
- Base model: Qwen/Qwen2.5-7B-Instruct
- Merge method: ties
- Merge density: 0.1
- Final stage epochs: 1
- Learning rate: 1e-05
- Final LoRA: r=16, alpha=16
- Max sequence length: 2024
## Datasets
- u-10bei/sft_alfworld_trajectory_dataset_v5
- u-10bei/dbbench_sft_dataset_react_v4
Additional distilled datasets were optionally included.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "takayosh/agentbenchfinal"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
```
## Sources & Terms (IMPORTANT)
Training data:
- u-10bei/sft_alfworld_trajectory_dataset_v5
- u-10bei/dbbench_sft_dataset_react_v4
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.
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