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qwen2.5-7b-instruct-sft-v4

Name: qwen2.5-7b-instruct-sft-v4
Availability: InStock

This repository provides a merged full model produced by supervised fine-tuning for AgentBench-oriented ALFWorld/DBBench robustness.

Training Objective

Improve strict action selection reliability for ALFWorld prompts and strengthen SQL error-recovery robustness for DBBench prompts.

Training Configuration

Method: SFT (Unsloth LoRA) + merge to full model
Base model ID: Qwen/Qwen2.5-7B-Instruct
LoRA: r=16, alpha=32, dropout=0.0
LoRA target modules: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
Output learn mode: from_marker (ACTION:)
Max sequence length: 4096
Max steps: 350
Epochs: 1
Learning rate: 1.0e-6
Per-device train batch size: 1
Per-device eval batch size: 2
Gradient accumulation steps: 32
Effective global batch size: 32
Warmup ratio: 0.03
Weight decay: 0.01
Eval/Save steps: 50 / 50

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "uchkw/qwen2.5-7b-instruct-sft-v4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

Training Data / Sources & License (IMPORTANT)

Primary source datasets:
- u-10bei/sft_alfworld_trajectory_dataset_v5
- u-10bei/dbbench_sft_dataset_react_v4
- u-10bei/dbbench_sft_dataset_react_v3
Data construction policy (concise):
- ALFWorld data was converted to strict one-line action supervision (ACTION: ...) with exact matching against AVAILABLE ACTIONS.
- Added ALF trap-guard augmentation (e.g., Task succeeded. in observation context) while preserving strict action targets.
- Added DBBench unknown-column recovery-oriented synthetic rows and duplicated unknown-column cases to stabilize recovery behavior.
- Mixed training ratio was controlled at approximately ALF:DB = 55:45.
Dataset scale (this model):
- Train samples: 36455
- Validation samples: 1918
- ALF strict-match in training set: 1.0
- ALF completion-verb ratio: 0.4711
- ALF trap ratio: 0.1500
- ALF toggle rows: 467
- DB unknown-column rows: 64 -> 128 (before/after duplication)
Compliance:
- Follow each source dataset card and license terms.
- Follow base model terms of use.