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: kiratan/qwen3-4b-agent-trajectory-lora
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
from peft import PeftModel
import torch

base = "kiratan/qwen3-4b-agent-trajectory-lora"
adapter = "your_id/your-repo"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)

Sources & Terms (IMPORTANT)

Training data: u-10bei/dbbench_sft_dataset_react, u-10bei/dbbench_sft_dataset_react_v2, u-10bei/dbbench_sft_dataset_react_v3, u-10bei/dbbench_sft_dataset_react_v4

Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License. Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.

kiratan/qwen3-4b-agent-trajectory-lora-1

qwen3-4b-agent-trajectory-lora

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

kiratan/
qwen3-4b-agent-trajectory-lora-1