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ekunish/llm2025-adv-exp016b-fix-format

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face

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exp016bfixformat

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

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ekunish/llm2025-adv-exp016b-fix-format"

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, u-10bei/dbbenchsftdatasetreactv4, data/synthetic/dbbench/train_micro50.jsonl

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