metadata
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/dbbench_sft_dataset_react_v4
- u-10bei/sft_alfworld_trajectory_dataset_v5
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- peft
- unsloth
- agent
- tool-use
- agentbench
- alfworld
- dbbench
qwen3-4b-agentbench-dbalf-lora
This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using LoRA + Unsloth for AgentBench-style multi-turn agent trajectories.
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:
- DBBench (database operation / SQL generation trajectories)
- ALFWorld (household task trajectories)
Loss is applied to all assistant turns in the trajectory, enabling the model to learn:
- environment observation
- action selection
- tool use / operation formatting
- recovery from intermediate errors
Training Data
- DBBench dataset:
u-10bei/dbbench_sft_dataset_react_v4 - ALFWorld dataset:
u-10bei/sft_alfworld_trajectory_dataset_v5 - Mixing ratio (pre-merge target): DB:ALF = 1:0
DB Oversampling (category-aware)
Enabled: False
DB category weights used during training-data preparation:
- counting: 1
- comparison: 1
- ranking: 1
- select: 1
- insert: 1
- update: 1
- other: 1
Training Configuration
- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: LoRA (full precision base)
- Max sequence length: 2048
- Epochs: 1.2
- Learning rate: 3e-06
- LoRA: r=32, alpha=64, dropout=0.0
- Per-device train batch size: 2
- Gradient accumulation: 4
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "AF0815/agentbench"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
base,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)
Notes
- This repository is intended for adapter-only distribution.
- Please ensure compliance with the base model license/terms in addition to this repository's license.
- If you publish evaluation results, it is recommended to report:
- AgentBench task split / seeds
- DBBench / ALFWorld mix ratio
- DB oversampling settings
- decoding settings
Sources & Terms (IMPORTANT)
Training data:
- u-10bei/dbbench_sft_dataset_react_v4
- u-10bei/sft_alfworld_trajectory_dataset_v5
Dataset license / terms:
- Please follow the original license and terms of each dataset repository.
- This adapter repository license (apache-2.0) applies to the adapter files in this repository.