metadata
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
- Train LoRA adapter on ALFWorld
- Train LoRA adapter on DBBench
- Merge adapters using
ties(density=0.1) - Apply additional stabilization fine-tuning (LoRA)
- 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
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.