Text Generation
Transformers
PyTorch
constrained-decoding
reachability
logit-processor
structured-generation
grammar-masking
dfa
fsm
Instructions to use uuugi/gclm-constrained-decoding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| import pytest | |
| import torch | |
| from core.fsm_builder import ReachabilityFSM | |
| def test_fsm_basic_reachability(): | |
| # Linear graph: 0 -> 1 -> 2 (Goal) | |
| vocab_size = 10 | |
| fsm = ReachabilityFSM(num_states=3, vocab_size=vocab_size) | |
| fsm.add_transition(0, token_id=1, to_state=1) | |
| fsm.add_transition(1, token_id=2, to_state=2) | |
| fsm.set_goal_states([2]) | |
| # Max steps = 2 | |
| reach = fsm.build_reachability(max_steps=2, allow_early_finish=True) | |
| # t = 0: only state 2 is True | |
| assert reach[0, 2].item() is True | |
| assert reach[0, 1].item() is False | |
| assert reach[0, 0].item() is False | |
| # t = 1: state 1 and 2 are True | |
| assert reach[1, 2].item() is True | |
| assert reach[1, 1].item() is True | |
| assert reach[1, 0].item() is False | |
| # t = 2: state 0, 1, 2 are all True | |
| assert reach[2, 0].item() is True | |
| assert reach[2, 1].item() is True | |
| assert reach[2, 2].item() is True | |
| def test_fsm_deadend_reachability(): | |
| # Branching graph: | |
| # 0 -> 1 -> 2 (Goal) via token 1, 2 (needs 2 steps) | |
| # 0 -> 3 -> 4 (Dead-end) via token 3, 4 (sink) | |
| vocab_size = 10 | |
| fsm = ReachabilityFSM(num_states=5, vocab_size=vocab_size) | |
| fsm.add_transition(0, token_id=1, to_state=1) | |
| fsm.add_transition(1, token_id=2, to_state=2) | |
| fsm.add_transition(0, token_id=3, to_state=3) | |
| fsm.add_transition(3, token_id=4, to_state=4) | |
| fsm.set_goal_states([2]) | |
| reach = fsm.build_reachability(max_steps=5, allow_early_finish=True) | |
| # States 3 and 4 should NEVER be reachable to goal | |
| for t in range(6): | |
| assert reach[t, 3].item() is False | |
| assert reach[t, 4].item() is False | |
| # State 0 is reachable only when t >= 2 | |
| assert reach[0, 0].item() is False | |
| assert reach[1, 0].item() is False | |
| assert reach[2, 0].item() is True | |
| assert reach[3, 0].item() is True | |
| def test_fsm_multi_goal(): | |
| # 0 -> 1 (Goal A), 0 -> 2 (Goal B) | |
| vocab_size = 5 | |
| fsm = ReachabilityFSM(num_states=3, vocab_size=vocab_size) | |
| fsm.add_transition(0, 1, 1) | |
| fsm.add_transition(0, 2, 2) | |
| fsm.set_goal_states([1, 2]) | |
| reach = fsm.build_reachability(max_steps=1) | |
| assert reach[0, 1].item() is True | |
| assert reach[0, 2].item() is True | |
| assert reach[0, 0].item() is False | |
| assert reach[1, 0].item() is True | |