Cgml
llama_index
Cgml | llama_index | |
---|---|---|
22 | 75 | |
39 | 31,389 | |
- | 5.3% | |
8.6 | 10.0 | |
4 months ago | 2 days ago | |
C++ | Python | |
GNU Lesser General Public License v3.0 only | 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.
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.
llama_index
- LlamaIndex: A data framework for your LLM applications
- FLaNK AI - 01 April 2024
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Show HN: Ragdoll Studio (fka Arthas.AI) is the FOSS alternative to character.ai
For anyone curious llamaindex's "prompt mixins", they're actually dead simple: https://github.com/run-llama/llama_index/blob/8a8324008764a7... - and maybe no longer supported.
I basically reinvented this wheel in ragdoll but made it more dynamic: https://github.com/bennyschmidt/ragdoll/blob/master/src/util...
- LlamaIndex is a data framework for your LLM applications
- How to verify that a snippet of Python code doesn't access protected members
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🆓 Local & Open Source AI: a kind ollama & LlamaIndex intro
Being able to plug third party frameworks (Langchain, LlamaIndex) so you can build complex projects
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I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros
Mistral Instruct does use a system prompt.
You can see the raw format here: https://www.promptingguide.ai/models/mistral-7b#chat-templat... and you can see how LllamaIndex uses it here (as an example): https://github.com/run-llama/llama_index/blob/1d861a9440cdc9...
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Top 5 Vector Database Videos of 2023 🎥
Learn how to use Milvus as persistent vector storage with LlamaIndex in under 5 minutes.
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What's going on in the Zilliz Universe? December 2023
▶️ Read Blog 📷 Watch Demo 🦙 Notebook using Pipelines inside LlamaIndex
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First 15 Open Source Advent projects
15. LlamaIndex | Github | tutorial
What are some alternatives?
PowerInfer - High-speed Large Language Model Serving on PCs with Consumer-grade GPUs
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
langchain - 🦜🔗 Build context-aware reasoning applications
mlx - MLX: An array framework for Apple silicon
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks
EmotiVoice - EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.
llamafile - Distribute and run LLMs with a single file.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
clspv - Clspv is a compiler for OpenCL C to Vulkan compute shaders
gpt-llama.cpp - A llama.cpp drop-in replacement for OpenAI's GPT endpoints, allowing GPT-powered apps to run off local llama.cpp models instead of OpenAI.