Instructions to use zechen-nlp/parallel-ttt-cot-qwen3-4b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use zechen-nlp/parallel-ttt-cot-qwen3-4b-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "zechen-nlp/parallel-ttt-cot-qwen3-4b-v1") - Notebooks
- Google Colab
- Kaggle
parallel-ttt-cot-qwen3-4b-v1
LoRA adapter checkpoint exported from epoch=0-step=4200.ckpt on top of Qwen/Qwen3-4B-Instruct-2507.
This repo intentionally stores the adapter separately from the base model so it can be resumed for later LoRA finetuning.
Load for inference or further finetuning
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model_name = "Qwen/Qwen3-4B-Instruct-2507"
adapter_name = "zechen-nlp/parallel-ttt-cot-qwen3-4b-v1"
tokenizer = AutoTokenizer.from_pretrained(adapter_name)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_name)
Included files
adapter_config.jsonadapter_model.safetensors- tokenizer files copied from the base Qwen3 repo, including
tokenizer_config.jsonwith the chat template generation_config.json
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Base model
Qwen/Qwen3-4B-Instruct-2507