motorhead
llama.cpp
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| motorhead | llama.cpp | |
|---|---|---|
| 10 | 769 | |
| 822 | 56,891 | |
| 2.6% | - | |
| 8.0 | 10.0 | |
| 9 days ago | 1 day ago | |
| Rust | 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.
motorhead
- Motorhead is a memory and information retrieval server for LLMs
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Comparison of Vector Databases
Metal [1] is another one on my radar. Their API looks super simple.
Disclosures: None
[1] https://getmetal.io
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Any Alternatives to Langchain?
Any alternatives? I found this Rust based project that might be interesting: https://github.com/getmetal/motorhead
- RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
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Langchain question and answer without openai
you could run motorhead on docker https://github.com/getmetal/motorhead
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How to use Enum with Vec to parse the mixed data vector from RedisSearch
The code is found using GitHub search FT.SEARCH inside https://github.com/getmetal/motorhead/blob/main/src/models.rs and adapted.
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Memory in production
All the examples that Langchain gives are for persisting memory locally which won't work in a serverless (statelesss) environment, and the one solution documented for stateless applications, getmetal/motorhead, is a containerized, Rust-based service we would have to run ourselves.
- Show HN: Motörhead, LLM Memory Server Built in Rust
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OpenAI Embeddings API alternative?
I've only just signed up and haven't had a chance to build anything with it yet, but this might be something to consider https://getmetal.io/
- Motörhead – memory and information retrieval server for LLMs
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
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Ollama 0.1.32: WizardLM 2, Mixtral 8x22B, macOS CPU/GPU model split
Ah, thanks for this! I can't edit my parent comment that you replied to any longer unfortunately.
As I said, I only compared the contributors graphs [0] and checked for overlaps. But those apparently only go back about year and only list at most 100 contributors ranked by number of commits.
[0]: https://github.com/ollama/ollama/graphs/contributors and https://github.com/ggerganov/llama.cpp/graphs/contributors
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KodiBot - Local Chatbot App for Desktop
KodiBot is a desktop app that enables users to run their own AI chat assistants locally and offline on Windows, Mac, and Linux operating systems. KodiBot is a standalone app and does not require an internet connection or additional dependencies to run local chat assistants. It supports both Llama.cpp compatible models and OpenAI API.
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Mixture-of-Depths: Dynamically allocating compute in transformers
There are already some implementations out there which attempt to accomplish this!
Here's an example: https://github.com/silphendio/sliced_llama
A gist pertaining to said example: https://gist.github.com/silphendio/535cd9c1821aa1290aa10d587...
Here's a discussion about integrating this capability with ExLlama: https://github.com/turboderp/exllamav2/pull/275
And same as above but for llama.cpp: https://github.com/ggerganov/llama.cpp/issues/4718#issuecomm...
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The lifecycle of a code AI completion
For those who might not be aware of this, there is also an open source project on GitHub called "Twinny" which is an offline Visual Studio Code plugin equivalent to Copilot: https://github.com/rjmacarthy/twinny
It can be used with a number of local model services. Currently for my setup on a NVIDIA 4090, I'm running both the base and instruct model for deepseek-coder 6.7b using 5_K_M Quantization GGUF files (for performance) through llama.cpp "server" where the base model is for completions and the instruct model for chat interactions.
llama.cpp: https://github.com/ggerganov/llama.cpp/
deepseek-coder 6.7b base GGUF files: https://huggingface.co/TheBloke/deepseek-coder-6.7B-base-GGU...
deepseek-coder 6.7b instruct GGUF files: https://huggingface.co/TheBloke/deepseek-coder-6.7B-instruct...
What are some alternatives?
lmql - A language for constraint-guided and efficient LLM programming.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
NeMo-Guardrails - NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.
gpt4all - gpt4all: run open-source LLMs anywhere
RasaGPT - 💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
kor - LLM(😽)
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
Abstract Feature Branch - abstract_feature_branch is a Ruby gem that provides a variation on the Branch by Abstraction Pattern by Paul Hammant and the Feature Toggles Pattern by Martin Fowler (aka Feature Flags) to enable Continuous Integration and Trunk-Based Development.
ggml - Tensor library for machine learning
rasa-haystack
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM