Loss is applied only to the final assistant output, while intermediate reasoning (Chain-of-Thought) is masked (assistant-only loss with CoT masking via output markers).

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 512
Epochs: 1
Learning rate: 1e-6
LoRA: r=64, alpha=128, dropout=0
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "your_id/qwen3-4b-sft-v0-cotv2-lr1e6-ep1"

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/structured_data_with_cot_dataset_512_v2

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.

Repository License: MIT

sabia0080/qwen3-4b-sft-v0-cotv2-lr1e6-ep1

qwen3-4b-sft-v0-cotv2-lr1e6-ep1

Training Objective

Training Configuration

Usage

sabia0080/
qwen3-4b-sft-v0-cotv2-lr1e6-ep1