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
1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3import torch
4
5base = "Qwen/Qwen2.5-7B-Instruct"
6adapter = "your_id/your-repo"
7
8tokenizer = AutoTokenizer.from_pretrained(base)
9model = AutoModelForCausalLM.from_pretrained(
10 base,
11 torch_dtype=torch.float16,
12 device_map="auto",
13)
14model = 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.cleaned.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.
