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
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import torch
from .fsm_builder import ReachabilityFSM
class FSMCompiler:
"""
Utility to compile high-level structure definitions into a ReachabilityFSM.
Supports token matching with HuggingFace Tokenizers.
"""
def __init__(self, vocab_size: int, tokenizer: Optional[Any] = None, device: str = "cpu"):
self.vocab_size = vocab_size
self.tokenizer = tokenizer
self.device = device
def _get_token_ids(self, text_or_tokens: Union[str, int, Sequence[int]]) -> List[int]:
"""Convert string or token IDs into a list of token IDs."""
if isinstance(text_or_tokens, int):
return [text_or_tokens]
if isinstance(text_or_tokens, (list, tuple)):
return list(text_or_tokens)
if isinstance(text_or_tokens, str):
if self.tokenizer is None:
raise ValueError("Tokenizer required to convert string to token IDs.")
tokens = self.tokenizer.encode(text_or_tokens, add_special_tokens=False)
return tokens
raise TypeError(f"Unsupported token specification: {type(text_or_tokens)}")
def build_synthetic_deadend_fsm(
self,
token_success_path: Sequence[int],
token_deadend_path: Sequence[int],
eos_token_id: int,
) -> ReachabilityFSM:
"""
Creates a synthetic dead-end trap FSM:
State 0 (A): Start
Path 1 (Success): 0 -> 1 -> 2 -> ... -> Goal (accepts token_success_path, then eos)
Path 2 (Dead-end): 0 -> D1 -> D2 -> ... -> Sink (accepts token_deadend_path, no goal)
"""
len_succ = len(token_success_path)
len_dead = len(token_deadend_path)
# State 0: Start
# States 1 to len_succ: Success path
# Goal state: len_succ + 1 (emits EOS and self-loops)
# Dead-end states: len_succ + 2 to len_succ + 1 + len_dead
# Dead-end sink: len_succ + 2 + len_dead
goal_state = len_succ + 1
num_states = goal_state + len_dead + 2
fsm = ReachabilityFSM(num_states=num_states, vocab_size=self.vocab_size, device=self.device)
# Success path
curr = 0
for i, tid in enumerate(token_success_path):
next_s = i + 1
fsm.add_transition(curr, tid, next_s)
curr = next_s
# Success path final step -> Goal
fsm.add_transition(curr, eos_token_id, goal_state)
# Goal state self loop
fsm.add_transition(goal_state, eos_token_id, goal_state)
# Dead-end path
curr = 0
dead_start = len_succ + 2
for i, tid in enumerate(token_deadend_path):
next_s = dead_start + i
fsm.add_transition(curr, tid, next_s)
curr = next_s
# Sink state for dead-end (no transition to goal)
sink = num_states - 1
fsm.add_transition(curr, eos_token_id, sink)
fsm.set_goal_states([goal_state])
return fsm
def build_strict_budget_json_fsm(
self,
open_bracket_tokens: Sequence[int],
key_value_tokens_list: List[Sequence[int]],
close_bracket_tokens: Sequence[int],
eos_token_id: int,
) -> ReachabilityFSM:
"""
Builds a JSON schema FSM with multiple optional fields and guaranteed closing:
- Must start with '{'
- Can generate key-value pairs in sequence or loop
- Can close with '}' and EOS at any point, but MUST close with '}' before EOS.
"""
# States:
# 0: Pre-open
# 1: Inside object (after open bracket)
# KV states: intermediate steps for generating keys & values
# Close state: After '}'
# Goal state: After EOS
# Let's create an FSM where:
# 0 --(open_bracket)--> 1
# 1 --(close_bracket)--> Close
# 1 --(KV_path)--> 1 (loop for next fields)
# Close --(eos)--> Goal (Goal self-loops on eos)
state_counter = 2
kv_routes = []
for kv in key_value_tokens_list:
route = []
for tid in kv:
route.append((state_counter, tid))
state_counter += 1
kv_routes.append(route)
close_state = state_counter
state_counter += 1
goal_state = state_counter
state_counter += 1
fsm = ReachabilityFSM(num_states=state_counter, vocab_size=self.vocab_size, device=self.device)
# 0 -> 1 on open bracket
for tok in open_bracket_tokens:
fsm.add_transition(0, tok, 1)
# 1 -> Close on close bracket
for tok in close_bracket_tokens:
fsm.add_transition(1, tok, close_state)
# KV branches from 1 and returning to 1
for route in kv_routes:
curr = 1
for i, (next_s, tid) in enumerate(route):
target = next_s if i < len(route) - 1 else 1 # loop back to state 1
fsm.add_transition(curr, tid, target)
curr = next_s
# Close -> Goal on EOS
fsm.add_transition(close_state, eos_token_id, goal_state)
# Goal self-loop
fsm.add_transition(goal_state, eos_token_id, goal_state)
fsm.set_goal_states([goal_state])
return fsm
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