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: morizon/qwen2.5-7b-agent-trajectory-lora_0228_run_9
Method: LoRA (full precision base)
Max sequence length: 2048
Epochs: 2
Learning rate: 1e-06
LoRA: r=32, alpha=64

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "morizon/qwen2.5-7b-agent-trajectory-lora_0228_run_9"
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_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.

morizon/qwen2.5-dbench_finalanswer_0301_run_17

qwen2.5-7b-dbbench-finalanswer-lora_0301_run_17

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

morizon/
qwen2.5-dbench_finalanswer_0301_run_17