Instructions to use uuugi/gclm-constrained-decoding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uuugi/gclm-constrained-decoding with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="uuugi/gclm-constrained-decoding")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("uuugi/gclm-constrained-decoding", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use uuugi/gclm-constrained-decoding with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "uuugi/gclm-constrained-decoding"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uuugi/gclm-constrained-decoding",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/uuugi/gclm-constrained-decoding

SGLang

How to use uuugi/gclm-constrained-decoding 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 "uuugi/gclm-constrained-decoding" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uuugi/gclm-constrained-decoding",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "uuugi/gclm-constrained-decoding" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "uuugi/gclm-constrained-decoding",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use uuugi/gclm-constrained-decoding with Docker Model Runner:
```
docker model run hf.co/uuugi/gclm-constrained-decoding
```

uuugi commited on 4 days ago

Commit

6afdcc7

verified ·

1 Parent(s): cc3b4ca

Add GitHub repo badges, clone options, and Paper PDF links

Browse files

Files changed (1) hide show

README.md +19 -3

README.md CHANGED Viewed

@@ -15,14 +15,18 @@ pipeline_tag: text-generation
 # Goal-Conditioned Reachability Logit Masker (GCLM)
 [![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
 [![PyTorch 2.0+](https://img.shields.io/badge/PyTorch-2.0+-ee4c2c.svg)](https://pytorch.org/)
-[![Transformers 4.36+](https://img.shields.io/badge/Transformers-4.36+-yellow.svg)](https://huggingface.co/docs/transformers)
 [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
 An ultra-fast, strictly **O(1)** runtime **Goal-Conditioned Reachability Logit Masking Engine** for Large Language Models.
 GCLM mathematically guarantees that an LLM will strictly reach designated goal/accepting states within a fixed token budget (`T_max`), **fundamentally preventing dead-end traps and truncated syntax failures**.
 ---
 ## 💡 Key Differences: GCLM vs. Forward DFA Maskers (Outlines / SGLang)
@@ -155,13 +159,21 @@ gclm_project/
 ## 🚀 Quick Start
 ### 1. Installation
 ```bash
-# Clone the repository from Hugging Face
 git clone https://huggingface.co/uuugi/gclm-constrained-decoding
 cd gclm-constrained-decoding
 pip install -r requirements.txt
 ```
 ### 2. Basic Usage with Hugging Face Transformers
 ```python
 import torch
@@ -226,7 +238,11 @@ python -m benchmarks.real_model_bench --model Qwen/Qwen2.5-0.5B
 ---
-## 📑 Citation & Author
 ```bibtex
 @article{an2026gclm,

 # Goal-Conditioned Reachability Logit Masker (GCLM)
+[![GitHub Repo](https://img.shields.io/badge/GitHub-Repository-181717.svg?logo=github)](https://github.com/uuuugi/Goal-Conditioned-Reachability-Logit-Masker)
+[![Paper PDF](https://img.shields.io/badge/Paper-PDF-b31b1b.svg?logo=adobeacrobatreader)](paper.pdf)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Repository-yellow)](https://huggingface.co/uuugi/gclm-constrained-decoding)
 [![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
 [![PyTorch 2.0+](https://img.shields.io/badge/PyTorch-2.0+-ee4c2c.svg)](https://pytorch.org/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
 An ultra-fast, strictly **O(1)** runtime **Goal-Conditioned Reachability Logit Masking Engine** for Large Language Models.
 GCLM mathematically guarantees that an LLM will strictly reach designated goal/accepting states within a fixed token budget (`T_max`), **fundamentally preventing dead-end traps and truncated syntax failures**.
+📄 **Paper**: [**Read / Download `paper.pdf`**](paper.pdf) &nbsp;|&nbsp; 💻 **GitHub**: [**uuuugi/Goal-Conditioned-Reachability-Logit-Masker**](https://github.com/uuuugi/Goal-Conditioned-Reachability-Logit-Masker)
 ---
 ## 💡 Key Differences: GCLM vs. Forward DFA Maskers (Outlines / SGLang)
 ## 🚀 Quick Start
 ### 1. Installation
+**From Hugging Face:**
 ```bash
 git clone https://huggingface.co/uuugi/gclm-constrained-decoding
 cd gclm-constrained-decoding
 pip install -r requirements.txt
 ```
+**From GitHub:**
+```bash
+git clone https://github.com/uuuugi/Goal-Conditioned-Reachability-Logit-Masker.git
+cd Goal-Conditioned-Reachability-Logit-Masker
+pip install -r requirements.txt
+```
 ### 2. Basic Usage with Hugging Face Transformers
 ```python
 import torch
 ---
+## 📑 Paper & Citation
+📄 **Paper PDF**: [**Download `paper.pdf`**](paper.pdf)
+💻 **GitHub Repository**: [**uuuugi/Goal-Conditioned-Reachability-Logit-Masker**](https://github.com/uuuugi/Goal-Conditioned-Reachability-Logit-Masker)
+🤗 **Hugging Face Model**: [**uuugi/gclm-constrained-decoding**](https://huggingface.co/uuugi/gclm-constrained-decoding)
 ```bibtex
 @article{an2026gclm,