ekunish/llm2025-adv-exp019-narrative-insert

exp019narrativeinsert

This repository provides a merged model fine-tuned from Qwen/Qwen2.5-7B-Instruct using LoRA + Unsloth.

Training Objective

This model is trained to improve multi-turn agent task performance on ALFWorld (household tasks) and DBBench (database operations).

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: 1
• —Learning rate: 1e-06
• —LoRA: r=64, alpha=128

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ekunish/llm2025-adv-exp019-narrative-insert"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

Sources & Terms

Training data: u-10bei/sftalfworldtrajectorydatasetv5, data/synthetic/dbbenchv4narrative/train.jsonl, data/synthetic/dbbenchmaxcount_15/train.jsonl

Dataset License: MIT License. Compliance: Users must comply with the MIT license and the base model's original terms of use.