The shimmytok project provides a pure Rust GGUF tokenizer compatible with llama.cpp and ready for WebAssembly.
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24 alternativesLMQL
LMQL provides a programming language for interactions with large language models.
About LMQL
LMQL is a programming language for interacting with large language models. It allows prompts to be written inside Python functions, using types, templates and where-clause constraints to control generated text, with results available as variables.
It supports nested queries for reuse of prompt components and Python control flow such as loops for building prompts. Code can be moved between backends including llama.cpp, OpenAI and Transformers with a single line change.
Key features
- Constrained variables with where clauses to limit output
- Typed variables for structured output, such as integers
- Nested queries for reusable prompt components
- Switch LLM backends with a single line change
- Python loops and control flow for prompt construction
- String interpolation to build multi-part prompts
Best for: Developers using Python to build structured, constraint-based prompts for large language models.
Good for
- Works with llama.cpp, OpenAI, and Transformers backends
- Enforces output limits with types and where-clause constraints
- Query programs run as Python functions with accessible variables
LMQL at a glance
- Website
- lmql.ai
- Category
- Development › Programming Languages
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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LMQL FAQ
Which backends can LMQL use?
llama.cpp, OpenAI, and Transformers, with switching by changing a single line of code.
How does LMQL work with Python?
Prompts are written inside Python functions using control flow and string interpolation, and generated values are accessible as variables.
What do nested queries do?
They allow modular local instructions and reuse of prompt components.
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