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zhongweixie
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zhongweixie
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This is the baseline model for the In-Place TTT research project at 64k context length. It uses the same:
The only difference is that it does not use In-Place Test-Time Training.
Compare this baseline with the TTT version to see the impact of In-Place TTT:
| Model | Type | Context | Steps |
|---|---|---|---|
| This Model | Baseline | 64k | 750 |
| 64k TTT | In-Place TTT | 64k | 1250 |
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch# Load modelmodel = AutoModelForCausalLM.from_pretrained("zhongweixie/inplace-ttt-qwen3-4b-64k-baseline",torch_dtype=torch.bfloat16,device_map="auto",trust_remote_code=True)tokenizer = AutoTokenizer.from_pretrained("zhongweixie/inplace-ttt-qwen3-4b-64k-baseline",trust_remote_code=True)# Generate text with long contextprompt = "Your very long context prompt here (up to 64k tokens)..."inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs,max_new_tokens=512,temperature=0.7,do_sample=True)print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Part of the In-Place TTT model family:
Apache 2.0 License
bibtex
@inproceedings{feng2026inplace,title = {In-Place Test-Time Training},author = {Feng, Guhao and Luo, Shengjie and Hua, Kai and Zhang, Ge and Huang, Wenhao and He, Di and Cai, Tianle},booktitle = {International Conference on Learning Representations (ICLR)},year = {2026},note = {Oral Presentation},url = {https://arxiv.org/abs/2604.06169}}
For questions or issues, please open an issue in the GitHub repository.
Model Card Author: Hansirui (zhongweixie)
Uploaded: 2026-09-04
Project: In-Place Test-Time Training