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: unsloth/Qwen2.5-7B-Instruct
Method: LoRA (full precision base)
Max sequence length: 3072
Epochs: 1
Learning rate: 1e-05
LoRA: r=64, alpha=128

Usage

python

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

base = "unsloth/Qwen2.5-7B-Instruct"
adapter = "Mountaingorillas/qwen2.5-7b-instruct-llm2025-lora"

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: Mountaingorillas/sft_dbbench_merged_v4_strict

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.

Mountaingorillas/Qwen-2.5-7B-Instruct-Agentbench-lora-db-Strict

qwen2.5-7b-instruct-llm2025-lora

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

Mountaingorillas/
Qwen-2.5-7B-Instruct-Agentbench-lora-db-Strict