qwen3-4b-agent-trajectory_2stageSFT_alfadm_dbweek

AgentBench LoRA Adapter (kuririrn/qwen3-4b-agent-trajectory_alf_admissible-lora-constraint_gen-dist_allign)

This repository provides a LoRA adapter fine-tuned from kuririrn/qwen3-4b-agent-trajectory_alf_admissible-lora-constraint_gen-dist_allign using LoRA + Unsloth.

This repository contains LoRA adapter weights only. The base model must be loaded separately.

Datasets

This adapter is trained on a single trajectory-style SFT dataset:

1. DBBench weak-type trajectories: kuririrn/dbbench_sft_dataset_react_v3v4_weaktypes
   • Subset of DBBench SFT data focusing on weak-type categories such as counting, comparison, aggregation-SUM, aggregation-MAX, INSERT.
   • These correspond to low-accuracy regimes in the baseline agent and are targeted to improve DBBench performance.

Training Objective

This adapter is trained to improve multi-turn agent task performance on DBBench (database operations), especially weak-type categories.

Loss is applied to all assistant turns so the model learns tool calls, SQL reasoning, and error recovery for DBBench environments.

Training Configuration

• Base model: kuririrn/qwen3-4b-agent-trajectory_alf_admissible-lora-constraint_gen-dist_allign
• Method: LoRA (full precision base)
• Max sequence length: 2048
• Epochs: 1
• Learning rate: 5e-07
• LoRA r: 64
• LoRA alpha: 128

Intended Use

This adapter is intended to be used as a tool-using agent for ALFWorld and/or DBBench-style tasks, depending on the training configuration:

• When trained only on ALFWorld, it focuses on household tasks.
• When trained only on DBBench, it focuses on database operations.
• When trained on both, it aims to balance both domains.

How to Load

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "kuririrn/qwen3-4b-agent-trajectory_alf_admissible-lora-constraint_gen-dist_allign",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("kuririrn/qwen3-4b-agent-trajectory_alf_admissible-lora-constraint_gen-dist_allign")

model = PeftModel.from_pretrained(
    base_model,
    "/content/lora_agentbench_qwen3_4b",
)

model.eval()

Limitations and Bias

This adapter is trained on synthetic and task-specific trajectories for ALFWorld and/or DBBench. It may:

• Overfit to the style of prompts and environments in these datasets.
• Hallucinate actions or SQL queries outside the training distribution.
• Fail on tasks that are very different from ALFWorld/DBBench.

Always validate the model's outputs, especially when using it for real-world decision making or database operations.