exllama
KoboldAI | exllama | |
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58 | 64 | |
150 | 2,609 | |
- | - | |
8.6 | 9.0 | |
7 months ago | 7 months ago | |
Python | Python | |
GNU Affero General Public License v3.0 | 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.
KoboldAI
- Any good models with 6gb vram?
- for some reasons, i can't download AI models from Kobold, how can i download them individually?
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ChatGPT users drop for the first time as people turn to uncensored chatbots
Pygmalion for chatting, Erebus for story writing, Wizard Vicuna Uncensored for general use including chatting, story writing or instructing, to name a few. There's lots more, but most are just the raw models that users are expected to load themselves so there isn't anything quite like ChatGPT where you just load up a single website and have all of them there. You'll either have to set up a local install using KoboldAI for running only on GPU, KoboldCPP for running only on CPU with optional splitting between CPU and GPU, or Oobabooga for CPU, GPU and splitting between CPU and GPU, if you have a powerful enough PC to run these models yourself (a PC with a 3090 can load up to a 30B model entirely in GPU, or a 65B model if you have 64GBs of RAM and a decently powerful CPU). If you don't have a powerful enough PC then you'll have to use something like KoboldAI Lite (website version) or SillyTavern to use the horde, a crowdsourced AI chatbot/LLM runner that lets people provide their hardware for others to use.
- Alr boys, how do I sign into Kobold?
- Poe down so here a meme
- GPU running out of memory despite meeting requirements?
- Any way to get a 13b model running on a 4070?
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Remote play .bat doesn't work for me how do i fix it?
INFO | __main__:general_startup:1312 - Running on Repo: https://github.com/0cc4m/koboldai Branch: latestgptq
- Help with KoboldAI API not generating responses
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Anyone tried this promising sounding release? WizardLM-33B-V1.0-Uncensored-SUPERHOT-8K
Occam seems to be trying or adding that into Kobold (https://github.com/0cc4m/KoboldAI/tree/4bit-plugin)
exllama
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Any way to optimally use GPU for faster llama calls?
not using exllama seems like the tremendous waste
- ExLlama: Memory efficient way to run Llama
- Ask HN: Cheapest hardware to run Llama 2 70B
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Llama Is Expensive
> We serve Llama on 2 80-GB A100 GPUs, as that is the minumum required to fit Llama in memory (with 16-bit precision)
Well there is your problem.
LLaMA quantized to 4 bits fits in 40GB. And it gets similar throughput split between dual consumer GPUs, which likely means better throughput on a single 40GB A100 (or a cheaper 48GB Pro GPU)
https://github.com/turboderp/exllama#dual-gpu-results
Also, I'm not sure which model was tested, but Llama 70B chat should have better performance than the base model if the prompting syntax is right. That was only reverse engineered from the Meta demo implementation recently.
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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/
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GPT-4 Details Leaked
Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .
If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...
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Multi-GPU questions
Exllama for example uses buffers on each card that reduce the amount of VRAM available for model and context, see here. https://github.com/turboderp/exllama/issues/121
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A simple repo for fine-tuning LLMs with both GPTQ and bitsandbytes quantization. Also supports ExLlama for inference for the best speed.
For inference step, this repo can help you to use ExLlama to perform inference on an evaluation dataset for the best throughput.
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GPT-4 API general availability
In terms of speed, we're talking about 140t/s for 7B models, and 40t/s for 33B models on a 3090/4090 now.[1] (1 token ~= 0.75 word) It's quite zippy. llama.cpp performs close on Nvidia GPUs now (but they don't have a handy chart) and you can get decent performance on 13B models on M1/M2 Macs.
You can take a look at a list of evals here: https://llm-tracker.info/books/evals/page/list-of-evals - for general usage, I think home-rolled evals like llm-jeopardy [2] and local-llm-comparison [3] by hobbyists are more useful than most of the benchmark rankings.
That being said, personally I mostly use GPT-4 for code assistance to that's what I'm most interested in, and the latest code assistants are scoring quite well: https://github.com/abacaj/code-eval - a recent replit-3b fine tune the human-eval results for open models (as a point of reference, GPT-3.5 gets 60.4 on pass@1 and 68.9 on pass@10 [4]) - I've only just started playing around with it since replit model tooling is not as good as llamas (doc here: https://llm-tracker.info/books/howto-guides/page/replit-mode...).
I'm interested in potentially applying reflexion or some of the other techniques that have been tried to even further increase coding abilities. (InterCode in particular has caught my eye https://intercode-benchmark.github.io/)
[1] https://github.com/turboderp/exllama#results-so-far
[2] https://github.com/aigoopy/llm-jeopardy
[3] https://github.com/Troyanovsky/Local-LLM-comparison/tree/mai...
[4] https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder
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Local LLMs GPUs
That's a 16GB GPU, you should be able to fit 13B at 4bit: https://github.com/turboderp/exllama
What are some alternatives?
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
llama.cpp - LLM inference in C/C++
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
SillyTavern - LLM Frontend for Power Users.
GPTQ-for-LLaMa - 4 bits quantization of LLaMa using GPTQ
GPTQ-for-LLaMa - 4 bits quantization of LLMs using GPTQ
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
llama-cpp-python - Python bindings for llama.cpp
text-generation-inference - Large Language Model Text Generation Inference
KoboldAI - KoboldAI is generative AI software optimized for fictional use, but capable of much more!
llama - Inference code for Llama models