grammars-v4
llama.cpp
| grammars-v4 | llama.cpp | |
|---|---|---|
| 29 | 773 | |
| 9,803 | 56,891 | |
| 0.8% | - | |
| 9.6 | 10.0 | |
| 2 days ago | 7 days ago | |
| ANTLR | C++ | |
| MIT License | MIT License |
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grammars-v4
- Operadores de adição e subtração
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Visual Basic for Applications Language Specification [pdf]
Perhaps the one from ANTLR's collection [0] is a good start (there are also others ANTLR VB6 grammars documented elsewhere). It does require knowing ANTLR, but that should be less effort for someone already familiar with language implementation, particularly, the visitor pattern (my favorite reference [1]).
[0] https://github.com/antlr/grammars-v4/tree/master/vb6
[1] https://craftinginterpreters.com/representing-code.html
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Postgres Language Server: Implementing the Parser
Where is the SQLite test suite, please? I'd be very interested.
There are already SQL grammars, check https://github.com/antlr/grammars-v4 specifically in here I think https://github.com/antlr/grammars-v4/tree/master/sql I contributed to one of them, and I wrote my own for some personal work. Be warned, it's very involved, very complex and MSSQL is rather ill-defined.
Names bracket identifiers) in SQL are bloody awful. Sometimes square brackets are even compulsory, and why you can usually replace [...] with the SQL standard "..." , not always! Trust me, it gets worse.
I don't find antlr grammars to be brittle, and while they can lose in performance (by how much I don't know, perhaps quite considerably) they are very easy to maintain and I am very fortunate to have antlr to work with.
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Llama: Add Grammar-Based Sampling
This grammar "library" was cited as an example of what the format could look like:.
https://github.com/antlr/grammars-v4
There is everything from assembly and C++ to glsl and scripting languages, arithmetic, games, and other weird formats.
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Structured Output from LLMs (Without Reprompting!)
> Which brings me to the other approach: steering the LLM's output __as it is generating tokens__
A relevant PR:
https://github.com/ggerganov/llama.cpp/pull/1773
The plan is to support arbitrary grammar files to constrain tokens as they are generated, like the ones here:
https://github.com/antlr/grammars-v4
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SQL-Parsing
Have a look at jooq - I know this has been used to rewrite SQL from one dialect to another, so it MUST be capable of collating code activity metrics. Look here. Otherwise, you might want to look into writing your own parser. ANTLR has a T-SQL dialect parser script here.
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How should I prepare for AI-driven changes in the industry as a Software Engineering Manager
Find a Perl grammar file for ANTLR, like https://github.com/antlr/grammars-v4/tree/master/perl Save the grammar file as Perl.g4 in your project. Now, you can create the Kotlin program: import org.antlr.v4.runtime.* import org.antlr.v4.runtime.tree.ParseTree import java.io.File
- Can you create a cpp file in a program like you could a txt file?
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DELD: An experimental HTTP-Client
Antlr is another option. You could generate a parser using the JSON antlr grammar.
- Are there any resources available to convert a code from Basic to C++? need to do this for the sake of an assignment. anything will be helpful
llama.cpp
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Better and Faster Large Language Models via Multi-Token Prediction
For anyone interested in exploring this, llama.cpp has an example implementation here:
https://github.com/ggerganov/llama.cpp/tree/master/examples/...
- Llama.cpp Bfloat16 Support
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Fine-tune your first large language model (LLM) with LoRA, llama.cpp, and KitOps in 5 easy steps
Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.
- GGML Flash Attention support merged into llama.cpp
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Phi-3 Weights Released
well https://github.com/ggerganov/llama.cpp/issues/6849
- Lossless Acceleration of LLM via Adaptive N-Gram Parallel Decoding
- Llama.cpp Working on Support for Llama3
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Embeddings are a good starting point for the AI curious app developer
Have just done this recently for local chat with pdf feature in https://recurse.chat. (It's a macOS app that has built-in llama.cpp server and local vector database)
Running an embedding server locally is pretty straightforward:
- Get llama.cpp release binary: https://github.com/ggerganov/llama.cpp/releases
- Mixtral 8x22B
- Llama.cpp: Improve CPU prompt eval speed
What are some alternatives?
ANTLR - ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
tree-sitter-sql - SQL grammar for tree-sitter
gpt4all - gpt4all: run open-source LLMs anywhere
lezer-snowsql
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
rewrite - Automated mass refactoring of source code.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
tree-sitter-sql - SQL syntax highlighting for tree-sitter
ggml - Tensor library for machine learning
go-mysql-server - A MySQL-compatible relational database with a storage agnostic query engine. Implemented in pure Go.
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM