turbopilot
ggml
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turbopilot | ggml | |
---|---|---|
15 | 69 | |
3,839 | 9,642 | |
- | - | |
10.0 | 9.8 | |
7 months ago | 4 days ago | |
C++ | C | |
BSD 3-clause "New" or "Revised" License | MIT License |
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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.
turbopilot
- New version of Turbopilot released!
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GGML for Falcoder7B, SantaCoder 1B, TinyStarCoder 160M
fyi https://github.com/ravenscroftj/turbopilot
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April 2023
TurboPilot: self-hosted copilot clone which uses the library behind llama.cpp to run the 6 Billion Parameter Salesforce Codegen model in 4GiB of RAM. (https://github.com/ravenscroftj/turbopilot)
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Which Models Best for Programming?
This repo has a potential
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[D] What Repos/Tools Should We Pay Attention To?
Right now https://github.com/ggerganov/llama.cpp is the dominant back-end for querying models, but forks and alternatives like https://github.com/ravenscroftj/turbopilot keep popping up. Increasingly, models submitted to huggingface explicitly note in their READMEs that the model is not compatible with llama.cpp, and that a different back-end must be used.
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newbie seeking impressive llama models, am i missing something?
There's turbopilot. I haven't tried it yet, but it looks promising.
- LocalAI: OpenAI compatible API to run LLM models locally on consumer grade hardware!
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LLM specialized in programming ?
Turbopilot | open source LLM code completion engine and Copilot alternative
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Locally running models like Chatgpt for Emacs?
This 6B parameters tool (based on README) could be runned with 4 Gb of RAM. https://github.com/ravenscroftj/turbopilot
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What models and setup is good for generating code
there is an interesting link https://github.com/ravenscroftj/turbopilot/wiki/Converting-and-Quantizing-The-Models , just wondering if anyone have done this with 16b and put the weights somewhere
ggml
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LLMs on your local Computer (Part 1)
git clone https://github.com/ggerganov/ggml cd ggml mkdir build cd build cmake .. make -j4 gpt-j ../examples/gpt-j/download-ggml-model.sh 6B
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GGUF, the Long Way Around
Cool. I was just learning about GGUF by creating my own parser for it based on the spec https://github.com/ggerganov/ggml/blob/master/docs/gguf.md (for educational purposes)
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Ask HN: People who switched from GPT to their own models. How was it?
If you don't care about the details of how those model servers work, then something that abstracts out the whole process like LM Studio or Ollama is all you need.
However, if you want to get into the weeds of how this actually works, I recommend you look up model quantization and some libraries like ggml[1] that actually do that for you.
[1] https://github.com/ggerganov/ggml
- GGUF File Format
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Google just shipped libggml from llama-cpp into its Android AICore
Because the library is called ggml, but it supports gguf.
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Q-Transformer
Apparently this guy like a bunch of others like https://github.com/ggerganov/ggml are implementing transformers from papers for people that want them. Pretty cool.
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[P] Inference Vision Transformer (ViT) in plain C/C++ with ggml
You can access it here: https://github.com/staghado/vit.cpp It has been added to the ggml library on GitHub: https://github.com/ggerganov/ggml
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Falcon 180B Released
https://github.com/ggerganov/ggml
One note is that prompt ingestion is extremely slow on CPU compared to GPU. So short prompts are fine (as tokens can be streamed once the prompt is ingested), but long prompts feel extremely sluggish.
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Stable Diffusion in pure C/C++
I did a quick run under profiler and on my AVX2-laptop the slowest part (>50%) was matrix multiplication (sgemm).
In current version of GGML if OpenBLAS is enabled, they convert matrices to FP32 before running sgemm.
If OpenBLAS is disabled, on AVX2 plaftorm they convert FP16 to FP32 on every FMA operation, which even worse (due to repetition). After that, both ggml_vec_dot_f16 and ggml_vec_dot_f32 took first place in profiler.
Source: https://github.com/ggerganov/ggml/blob/master/src/ggml.c#L10...
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Accessing Llama 2 from the command-line with the LLM-replicate plugin
For those getting started, the easiest one click installer I've used is Nomic.ai's gpt4all: https://gpt4all.io/
This runs with a simple GUI on Windows/Mac/Linux, leverages a fork of llama.cpp on the backend and supports GPU acceleration, and LLaMA, Falcon, MPT, and GPT-J models. It also has API/CLI bindings.
I just saw a slick new tool https://ollama.ai/ that will let you install a llama2-7b with a single `ollama run llama2` command that has a very simple 1-click installer for Apple Silicon Mac (but need to build from source for anything else atm). It looks like it only supports llamas OOTB but it also seems to use llama.cpp (via Go adapter) on the backend - it seemed to be CPU-only on my MBA, but I didn't poke too much and it's brand new, so we'll see.
For anyone on HN, they should probably be looking at https://github.com/ggerganov/llama.cpp and https://github.com/ggerganov/ggml directly. If you have a high-end Nvidia consumer card (3090/4090) I'd highly recommend looking into https://github.com/turboderp/exllama
For those generally confused, the r/LocalLLaMA wiki is a good place to start: https://www.reddit.com/r/LocalLLaMA/wiki/guide/
I've also been porting my own notes into a single location that tracks models, evals, and has guides focused on local models: https://llm-tracker.info/
What are some alternatives?
tabby - Self-hosted AI coding assistant
llama.cpp - LLM inference in C/C++
fauxpilot - FauxPilot - an open-source alternative to GitHub Copilot server
alpaca.cpp - Locally run an Instruction-Tuned Chat-Style LLM
prompt-engineering - ChatGPT Prompt Engineering for Developers - deeplearning.ai
alpaca-lora - Instruct-tune LLaMA on consumer hardware
telegram-chatgpt-concierge-bot - Interact with OpenAI's ChatGPT via Telegram and Voice.
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
simpleAI - An easy way to host your own AI API and expose alternative models, while being compatible with "open" AI clients.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
llm-apex-agents - Run Large Language Model "Agents" in Salesforce apex
llm - An ecosystem of Rust libraries for working with large language models