base_model: Qwen/Qwen2.5-7B-Instruct
datasets:
- u-10bei/sft_alfworld_trajectory_dataset_v5
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- agent
- tool-use
- alfworld
- dbbench
qwen25_7b_lora_agentbench_v21
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: 2
- 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
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",
)