Cgml
llamafile
Cgml | llamafile | |
---|---|---|
22 | 35 | |
39 | 15,120 | |
- | 27.7% | |
8.6 | 9.6 | |
4 months ago | about 17 hours ago | |
C++ | C++ | |
GNU Lesser General Public License v3.0 only | GNU General Public License v3.0 or later |
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.
Cgml
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Asynchronous Programming in C#
> Meant no offense
None taken.
> computervison project in c#
Yeah, for CV applications nuget.org is indeed not particularly great. Very few people are using C# for these things, people typically choose something else like Python and OpenCV.
BTW, same applies to ML libraries, most folks are using Python/Torch/CUDA stack. For that hobby project https://github.com/Const-me/Cgml/ I had to re-implement the entire tech stack in C#/C++/HLSL.
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Groq CEO: 'We No Longer Sell Hardware'
> If there is a future with this idea, its gotta be just shipping the LLM with game right?
That might be a nice application for this library of mine: https://github.com/Const-me/Cgml/
That’s an open source Mistral ML model implementation which runs on GPUs (all of them, not just nVidia), takes 4.5GB on disk, uses under 6GB of VRAM, and optimized for interactive single-user use case. Probably fast enough for that application.
You wouldn’t want in-game dialogues with the original model though. Game developers would need to finetune, retrain and/or do something else with these weights and/or my implementation.
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Ask HN: How to get started with local language models?
If you just want to run Mistral on Windows, you could try my port: https://github.com/Const-me/Cgml/tree/master/Mistral/Mistral...
The setup is relatively easy: install .NET runtime, download 4.5 GB model file from BitTorrent, unpack a small ZIP file and run the EXE.
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OpenAI postmortem – Unexpected responses from ChatGPT
Speaking about random sampling during inference, most ML models are doing it rather inefficiently.
Here’s a better way: https://github.com/Const-me/Cgml/blob/master/Readme.md#rando...
My HLSL is easily portable to CUDA, which has `__syncthreads` and `atomicInc` intrinsics.
- Nvidia's Chat with RTX is a promising AI chatbot that runs locally on your PC
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
I did a few times with Direct3D 11 compute shaders. Here’s an open-source example: https://github.com/Const-me/Cgml
Pretty sure Vulkan gonna work equally well, at the very least there’s an open source DXVK project which implements D3D11 on top of Vulkan.
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Brave Leo now uses Mixtral 8x7B as default
Here’s an example of a custom 4 bits/weight codec for ML weights:
https://github.com/Const-me/Cgml/blob/master/Readme.md#bcml1...
llama.cpp does it slightly differently but still, AFAIK their quantized data formats are conceptually similar to my codec.
- Efficient LLM inference solution on Intel GPU
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Vcc – The Vulkan Clang Compiler
> the API was high-friction due to the shader language, and the glue between shader and CPU
Direct3D 11 compute shaders share these things with Vulkan, yet D3D11 is relatively easy to use. For example, see that library which implements ML-targeted compute shaders for C# with minimal friction: https://github.com/Const-me/Cgml The backend implemented in C++ is rather simple, just binds resources and dispatches these shaders.
I think the main usability issue with Vulkan is API design. Vulkan was only designed with AAA game engines in mind. The developers of these game engines have borderline unlimited budgets, and their requirements are very different from ordinary folks who want to leverage GPU hardware.
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I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros
Minor update https://github.com/Const-me/Cgml/releases/tag/1.1a Can’t edit that comment anymore, too late.
llamafile
- FLaNK-AIM Weekly 06 May 2024
- llamafile v0.8
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Mistral AI Launches New 8x22B Moe Model
I think the llamafile[0] system works the best. Binary works on the command line or launches a mini webserver. Llamafile offers builds of Mixtral-8x7B-Instruct, so presumably they may package this one up as well (potentially a quantized format).
You would have to confirm with someone deeper in the ecosystem, but I think you should be able to run this new model as is against a llamafile?
[0] https://github.com/Mozilla-Ocho/llamafile
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Apple Explores Home Robotics as Potential 'Next Big Thing'
Thermostats: https://www.sinopetech.com/en/products/thermostat/
I haven't tried running a local text-to-speech engine backed by an LLM to control Home Assistant. Maybe someone is working on this already?
TTS: https://github.com/SYSTRAN/faster-whisper
LLM: https://github.com/Mozilla-Ocho/llamafile/releases
LLM: https://huggingface.co/TheBloke/Nous-Hermes-2-Mixtral-8x7B-D...
It would take some tweaking to get the voice commands working correctly.
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LLaMA Now Goes Faster on CPUs
While I did not succeed in making the matmul code from https://github.com/Mozilla-Ocho/llamafile/blob/main/llamafil... work in isolation, I compared eigen, openblas, and mkl: https://gist.github.com/Dobiasd/e664c681c4a7933ef5d2df7caa87...
In this (very primitive!) benchmark, MKL was a bit better than eigen (~10%) on my machine (i5-6600).
Since the article https://justine.lol/matmul/ compared the new kernels with MLK, we can (by transitivity) compare the new kernels with Eigen this way, at least very roughly for this one use-case.
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Llamafile 0.7 Brings AVX-512 Support: 10x Faster Prompt Eval Times for AMD Zen 4
Yes, they're just ZIP files that also happen to be actually portable executables.
https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file...
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Show HN: I made an app to use local AI as daily driver
have you seen llamafile[0]?
[0] https://github.com/Mozilla-Ocho/llamafile
- FLaNK Stack 26 February 2024
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Gemma.cpp: lightweight, standalone C++ inference engine for Gemma models
llama.cpp has integrated gemma support. So you can use llamafile for this. It is a standalone executable that is portable across most popular OSes.
https://github.com/Mozilla-Ocho/llamafile/releases
So, download the executable from the releases page under assets. You want either just main or just server. Don't get the huge ones with the model inlined in the file. The executable is about 30MB in size,
https://github.com/Mozilla-Ocho/llamafile/releases/download/...
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Ollama releases OpenAI API compatibility
The improvements in ease of use for locally hosting LLMs over the last few months have been amazing. I was ranting about how easy https://github.com/Mozilla-Ocho/llamafile is just a few hours ago [1]. Now I'm torn as to which one to use :)
1: Quite literally hours ago: https://euri.ca/blog/2024-llm-self-hosting-is-easy-now/
What are some alternatives?
PowerInfer - High-speed Large Language Model Serving on PCs with Consumer-grade GPUs
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
ollama-webui - ChatGPT-Style WebUI for LLMs (Formerly Ollama WebUI) [Moved to: https://github.com/open-webui/open-webui]
mlx - MLX: An array framework for Apple silicon
langchain - 🦜🔗 Build context-aware reasoning applications
EmotiVoice - EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
LLaVA - [NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
clspv - Clspv is a compiler for OpenCL C to Vulkan compute shaders
llama.cpp - LLM inference in C/C++
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability
safetensors - Simple, safe way to store and distribute tensors