from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "kevineen/Qwen3-4B-instruct-2507-sft-512v2-neftune10-es"

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

With Unsloth (Faster)

python

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="kevineen/Qwen3-4B-instruct-2507-sft-512v2-neftune10-es",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

License

This adapter is released under the Apache 2.0 License.

Sources & Terms (IMPORTANT)

Training data: u-10bei/structured_data_with_cot_dataset_512_v2

Dataset License: CC-BY-4.0. This dataset is used and distributed under the terms of the CC-BY-4.0.

Compliance: Users must comply with the CC-BY-4.0 (including copyright notice) and the base model's original terms of use.

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