qwen2.5-7b-instruct-sft-v1

This repository provides a merged full model produced by supervised fine-tuning for task-oriented instruction following.

Training Objective

Improve instruction following, action consistency, and response reliability in practical workflows.

Training Configuration

• Method: SFT (TRL SFTTrainer + Transformers, full-model)
• Max sequence length: 1024
• Max steps: 6
• Epochs: 1
• Learning rate: 2e-6
• Per-device train batch size: 1
• Gradient accumulation steps: 8
• Effective global batch size: 8
• Training schedule: Step-based (max_steps takes precedence in Trainer).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "uchkw/qwen2.5-7b-instruct-sft-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

Training Data / Sources & License (IMPORTANT)

• Datasets: rule-based original synthetic data
• Compliance: Users must comply with the base model's terms of use.

Training Plan Summary

• Objective: Push DB score upward aggressively while preserving ALF above baseline.
• Strategy:
  • Increase DB rows strongly and keep action rows moderate.
  • Use stronger SQL stage and very light action stage.