Instructions to use Tioe/LaTER-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tioe/LaTER-14B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Tioe/LaTER-14B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Tioe/LaTER-14B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Tioe/LaTER-14B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tioe/LaTER-14B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tioe/LaTER-14B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Tioe/LaTER-14B
- SGLang
How to use Tioe/LaTER-14B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Tioe/LaTER-14B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tioe/LaTER-14B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Tioe/LaTER-14B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tioe/LaTER-14B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Tioe/LaTER-14B with Docker Model Runner:
docker model run hf.co/Tioe/LaTER-14B
LaTER-14B
LaTER-14B is a latent-reasoning language model trained from Qwen3-14B. The model modifies the original Qwen3-14B architecture for LaTER-style latent exploration and explicit verification, then trains the modified architecture to support efficient test-time reasoning.
This checkpoint is not intended to be loaded as a vanilla Qwen3ForCausalLM model. Please refer to the LaTER code examples at https://github.com/TioeAre/LaTER for the required model implementation and inference usage.
Model Details
- Base model:
Qwen/Qwen3-14B - License: Apache-2.0
Usage
Use the model with the custom LaTER implementation rather than the default Qwen3 Transformers class. Example code and any required custom model registration should follow the repository:
https://github.com/TioeAre/LaTER
Intended Use
This model is intended for research on efficient test-time reasoning, latent exploration, and explicit verification.
Citation
@misc{li2026later,
title={LaTER: Efficient Test-Time Reasoning via Latent Exploration and Explicit Verification},
author={Xuan Li and Yining Wang and Yuchen Liu and Guanjun Liu and Delai Qiu and Shengping Liu and Jiaen Liang and Wei Huang and Jun Yu and Junnan Zhu},
year={2026},
eprint={2605.07315},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2605.07315},
}
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