SillyTavern
exllamav2
SillyTavern | exllamav2 | |
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76 | 17 | |
5,930 | 2,935 | |
8.5% | - | |
10.0 | 9.8 | |
5 days ago | 3 days ago | |
JavaScript | Python | |
GNU Affero General Public License v3.0 | MIT License |
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SillyTavern
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Claude 3 beats GPT-4 on Aider's code editing benchmark β aider
Right, but it's certainly easier for people who might not even know what "API" stands for, and that's quite nifty. As far as self-hosted frontends go, I can personally recommend SillyTavern[1] in the browser, ChatterUI[2] on mobile, and ShellGPT[3] for CLI. LobeChat looks pretty cool, though! I'll definitely check it out.
[1] https://github.com/SillyTavern/SillyTavern
[2] https://github.com/Vali-98/ChatterUI
[3] https://github.com/TheR1D/shell_gpt
- FLaNK AI for 11 March 2024
- Show HN: I made an app to use local AI as daily driver
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Group chats vs online defined characters, token efficiency question
I don't think there is any enumeration for {{char}} macros. Here is some good discussion on the subject.
- SillyTavern 1.11.0 has been released
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Is possible to run local voice chat agent? If yes what GPU do i Need with 500β¬ budget?
As for SillyTavern, you need the main SillyTavern frontend and SillyTavern-extras (for TTS, STT, etc.) They're pretty easy to install. SillyTavern connects to oobabooga and SillyTavern-extras via API.
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What do you use to run your models?
Finally, no matter what backend I use, I need it to be compatible with my power-user frontend, SillyTavern. That way I always use the same UI, with the characters I created and extensions I want, e. g. web search, XTTS text-to-speech and Whisper speech recognition for real-time voice chat - and all of that local!
- SillyTavern 1.10.10 has been released
- LM Studio β Discover, download, and run local LLMs
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πΊπ¦ββ¬ LLM Comparison/Test: Mistral 7B Updates (OpenHermes 2.5, OpenChat 3.5, Nous Capybara 1.9)
SillyTavern v1.10.5 frontend (not the latest as I don't want to upgrade mid-test)
exllamav2
- Running Llama3 Locally
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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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What do you use to run your models?
Sorry, I'm somewhat familiar with this term (I've seen it as a model loader in Oobabooga), but still not following the correlation here. Are you saying I should instead be using this project in lieu of llama.cpp? Or are you saying that there is, perhaps, an exllamav2 "extension" or similar within llama.cpp that I can use?
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I just started having problems with the colab again. I get errors and it just stops. Help?
EDIT: I reported the bug to the exllamav2 Github. It's actually already fixed, just not on any current built release.
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Yi-34B-200K works on a single 3090 with 47K context/4bpw
install exllamav2 from git with pip install git+https://github.com/turboderp/exllamav2.git. Make sure you have flash attention 2 as well.
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Tested: ExllamaV2's max context on 24gb with 70B low-bpw & speculative sampling performance
Recent releases for exllamav2 brings working fp8 cache support, which I've been very excited to test. This feature doubles the maximum context length you can run with your model, without any visible downsides.
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Show HN: Phind Model beats GPT-4 at coding, with GPT-3.5 speed and 16k context
Without batching, I was actually thinking that's kind of modest.
ExllamaV2 will get 48 tokens/s on a 4090, which is much slower/cheaper than an H100:
https://github.com/turboderp/exllamav2#performance
I didn't test codellama, but the 3090 TI figures are in the ballpark of my generation speed on a 3090.
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Guide for Llama2 70b model merging and exllama2 quantization
First, you need the convert.py script from turboderp's Exllama2 repo. You can read all about the convert.py arguments here.
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LLM Falcon 180B Needs 720GB RAM to Run
> brute aggressive quantization
Cutting edge quantization like ExLlama's EX2 is far from brute force: https://github.com/turboderp/exllamav2#exl2-quantization
> The format allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. Moreover, it's possible to apply multiple quantization levels to each linear layer, producing something akin to sparse quantization wherein more important weights (columns) are quantized with more bits. The same remapping trick that lets ExLlama work efficiently with act-order models allows this mixing of formats to happen with little to no impact on performance. Parameter selection is done automatically by quantizing each matrix multiple times, measuring the quantization error (with respect to the chosen calibration data) for each of a number of possible settings, per layer. Finally, a combination is chosen that minimizes the maximum quantization error over the entire model while meeting a target average bitrate.
Llama.cpp is also working on a feature that let's a small model "guess" the output of a big model which then "checks" it for correctness. This is more of a performance feature, but you could also arrange it to accelerate a big model on a small GPU.
- 70B Llama 2 at 35tokens/second on 4090
What are some alternatives?
TavernAI - TavernAI for nerds [Moved to: https://github.com/Cohee1207/SillyTavern]
llama.cpp - LLM inference in C/C++
character-editor - Create, edit and convert AI character files for CharacterAI, Pygmalion, Text Generation, KoboldAI and TavernAI
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
TavernAI - Atmospheric adventure chat for AI language models (KoboldAI, NovelAI, Pygmalion, OpenAI chatgpt, gpt-4)
ChatGPT-AutoExpert - ππ§ π¬ Supercharged Custom Instructions for ChatGPT (non-coding) and ChatGPT Advanced Data Analysis (coding).
SillyTavern-extras - Extensions API for SillyTavern [Moved to: https://github.com/SillyTavern/SillyTavern-extras]
OmniQuant - [ICLR2024 spotlight] OmniQuant is a simple and powerful quantization technique for LLMs.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
BlockMerge_Gradient - Merge Transformers language models by use of gradient parameters.
SillyTavern-Extras - Extensions API for SillyTavern.
gptq - Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers".