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

base = "fujiki/sft_0041_maxSeq1k_lr1e5_3ep"
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/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.

fn-aka-mur/sft_0043_cont0041_maxSeq1k_lr1e5_1ep

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

fn-aka-mur/
sft_0043_cont0041_maxSeq1k_lr1e5_1ep