Chat2DB
llama.cpp
Chat2DB | llama.cpp | |
---|---|---|
5 | 792 | |
14,054 | 60,282 | |
5.1% | - | |
9.9 | 10.0 | |
10 days ago | about 7 hours ago | |
Java | C++ | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Chat2DB
- LLMs and SQL
- AI-driven data management platform
- Show HN: AI-driven data development and analysis platform
- FLaNK Stack Weekly 12 February 2024
-
πͺΈ6 Text2SQL Tools to Write Stunning SQL for you βοΈ
Chat2DB aims to be a general-purpose SQL client and reporting tool that incorporates AI capabilities from the start. It supports connection to a handful of databases including MySQL, Postgres, Oracle, SQL Server, SQLite, ClickHouse and more.
llama.cpp
-
Apple Intelligence, the personal intelligence system
> Doing everything on-device would result in a horrible user experience. They might as well not participate in this generative AI rush at all if they hoped to keep it on-device.
On the contrary, I'm shocked over the last few months how "on device" on a Macbook Pro or Mac Studio competes plausibly with last year's early GPT-4, leveraging Llama 3 70b or Qwen2 72b.
There are surprisingly few things you "need" 128GB of so-called "unified RAM" for, but with M-series processors and the memory bandwidth, this is a use case that shines.
From this thread covering performance of llama.cpp on Apple Silicon M-series β¦
https://github.com/ggerganov/llama.cpp/discussions/4167
β¦ "Buy as much memory as you can afford would be my bottom line!"
-
Partial Outage on Claude.ai
I'd love to use local models, but seems like most of the easy to use software out there (LM Studio, Backyard AI, koboldcpp) doesn't really play all that nicely with my Intel Arc GPU and it's painfully slow on my Ryzen 5 4500. Even my M1 MacBook isn't that fast at generating text with even 7B models.
I wonder if llama.cpp with SYCL could help, will have to try it out: https://github.com/ggerganov/llama.cpp/blob/master/README-sy...
But even if that worked, I'd still have the problem that IDEs and whatever else I have open already eats most of the 32 GB of RAM my desktop PC has. Whereas if I ran a small code model on the MacBook and connected to it through my PC, it'd still probably be too slow for autocomplete, when compared to GitHub Copilot and less accurate than ChatGPT or Phind for most stuff.
-
Why YC Went to DC
You're correct if you're focused exclusively on the work surrounding building foundation models to begin with. But if you take a broader view, having open models that we can legally fine tune and hack with locally has created a large and ever-growing community of builders and innovators that could not exist without these open models. Just take a look at projects like InvokeAI [0] in the image space or especially llama.cpp [1] in the text generation space. These projects are large, have lots of contributors, move very fast, and drive a lot of innovation and collaboration in applying AI to various domains in a way that simply wouldn't be possible without the open models.
[0] https://github.com/invoke-ai/InvokeAI
[1] https://github.com/ggerganov/llama.cpp
- Show HN: Open-Source Load Balancer for Llama.cpp
-
RAG with llama.cpp and external API services
The first example will build an Embeddings database backed by llama.cpp vectorization.
-
Ask HN: I have many PDFs β what is the best local way to leverage AI for search?
and at some point (https://github.com/ggerganov/llama.cpp/issues/7444)
-
Deploying llama.cpp on AWS (with Troubleshooting)
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp LLAMA_CUDA=1 make -j
-
Devoxx Genie Plugin : an Update
I focused on supporting Ollama, GPT4All, and LMStudio, all of which run smoothly on a Mac computer. Many of these tools are user-friendly wrappers around Llama.cpp, allowing easy model downloads and providing a REST interface to query the available models. Last week, I also added "ππΌ Jan" support because HuggingFace has endorsed this provider out-of-the-box.
-
Mistral Fine-Tune
The output of the LLM is not just one token, but a statistical distribution across all possible output tokens. The tool you use to generate output will sample from this distribution with various techniques, and you can put constraints on it like not being too repetitive. Some of them support getting very specific about the allowed output format, e.g. https://github.com/ggerganov/llama.cpp/blob/master/grammars/... So even if the LLM says that an invalid token is the most likely next token, the tool will never select it for output. It will only sample from valid tokens.
- Distributed LLM Inference with Llama.cpp
What are some alternatives?
ali-dbhub - ε·²θΏη§»ζ°δ»εΊοΌζ€ηζ¬ε°δΈεη»΄ζ€
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
IOPaint - Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.
gpt4all - gpt4all: run open-source LLMs anywhere
electric - Local-first sync layer for web and mobile apps. Build reactive, realtime, local-first apps directly on Postgres.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
vanna - π€ Chat with your SQL database π. Accurate Text-to-SQL Generation via LLMs using RAG π.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
openai-java - OpenAI Api Client in Java
ggml - Tensor library for machine learning
RustPython - A Python Interpreter written in Rust
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM