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/sft_alfworld_trajectory_dataset_v5, u-10bei/dbbench_sft_dataset_react_v4, 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.