vllm
nerd-dictation
vllm | nerd-dictation | |
---|---|---|
31 | 28 | |
19,344 | 1,174 | |
12.6% | - | |
9.9 | 2.9 | |
2 days ago | about 1 month ago | |
Python | Python | |
Apache License 2.0 | GNU General Public License v3.0 only |
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vllm
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AI leaderboards are no longer useful. It's time to switch to Pareto curves
I guess the root cause of my claim is that OpenAI won't tell us whether or not GPT-3.5 is an MoE model, and I assumed it wasn't. Since GPT-3.5 is clearly nondeterministic at temp=0, I believed the nondeterminism was due to FPU stuff, and this effect was amplified with GPT-4's MoE. But if GPT-3.5 is also MoE then that's just wrong.
What makes this especially tricky is that small models are truly 100% deterministic at temp=0 because the relative likelihoods are too coarse for FPU issues to be a factor. I had thought 3.5 was big enough that some of its token probabilities were too fine-grained for the FPU. But that's probably wrong.
On the other hand, it's not just GPT, there are currently floating-point difficulties in vllm which significantly affect the determinism of any model run on it: https://github.com/vllm-project/vllm/issues/966 Note that a suggested fix is upcasting to float32. So it's possible that GPT-3.5 is using an especially low-precision float and introducing nondeterminism by saving money on compute costs.
Sadly I do not have the money[1] to actually run a test to falsify any of this. It seems like this would be a good little research project.
[1] Or the time, or the motivation :) But this stuff is expensive.
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Mistral AI Launches New 8x22B Moe Model
The easiest is to use vllm (https://github.com/vllm-project/vllm) to run it on a Couple of A100's, and you can benchmark this using this library (https://github.com/EleutherAI/lm-evaluation-harness)
- FLaNK AI for 11 March 2024
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Show HN: We got fine-tuning Mistral-7B to not suck
Great question! scheduling workloads onto GPUs in a way where VRAM is being utilised efficiently was quite the challenge.
What we found was the IO latency for loading model weights into VRAM will kill responsiveness if you don't "re-use" sessions (i.e. where the model weights remain loaded and you run multiple inference sessions over the same loaded weights).
Obviously projects like https://github.com/vllm-project/vllm exist but we needed to build out a scheduler that can run a fleet of GPUs for a matrix of text/image vs inference/finetune sessions.
disclaimer: I work on Helix
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Mistral CEO confirms 'leak' of new open source AI model nearing GPT4 performance
FYI, vLLM also just added experiment multi-lora support: https://github.com/vllm-project/vllm/releases/tag/v0.3.0
Also check out the new prefix caching, I see huge potential for batch processing purposes there!
- VLLM Sacrifices Accuracy for Speed
- Easy, fast, and cheap LLM serving for everyone
- vllm
- Mixtral Expert Parallelism
- Mixtral 8x7B Support
nerd-dictation
- why nerd-dictation support in NixOS is stuck ?
- Is anyone doing always-on voice to text with a local llama at home?
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Apollo dev posts backend code to Git to disprove Reddit’s claims of scrapping and inefficiency
nerd-dictation
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How to use notion in gnome
There's no built-in way of doing this in GNOME, but you might already get a bit further with tools like https://github.com/ideasman42/nerd-dictation
- What voice transcriber do you use?
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Disability accessibility tools for Linux such as eyetrackers and voice commands?
I'm not familiar with Talon so I don't know if this is a suitable suggestion but nerd-dictation seemed to have been well received here when it was last promoted and it looks like it's still in active development.
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Voice Control was supposed to be the Future. Is Linux lagging behind?
TBF Microsoft dropped IE, windows phone... that is not uncommon. But the OP is right, maybe not much for voice control but for dictation certainly. The FLOSS community is always far behind and thus always struggle with new technologies. We should be prepared. Since you've mentioned small open source project here's a demo of NerdDitaction. FYI Linux do have mobile devices developing.
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I've made voice input for Linux that I use instead of a keyboard and mouse
Yeah you get me. I did have RSI which was amplified by my other issue, but it was that issue that progressed and why can't type now, not RSI. I'd be interested in hearing about using numen in combination with typing, but it's likely not ideal yet. Maybe just using speech to text for some things could help? It's not my project but there's: https://github.com/ideasman42/nerd-dictation that uses the same speech recognition as numen.
- Voice to text for Linux
- nerd-dictation: Simple, hackable offline speech to text - using the VOSK-API.
What are some alternatives?
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
CTranslate2 - Fast inference engine for Transformer models
recasepunc - Model for recasing and repunctuating ASR transcripts
lmdeploy - LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
cursorless - Don't let the cursor slow you down
Llama-2-Onnx
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
tritony - Tiny configuration for Triton Inference Server
kaldi-active-grammar - Python Kaldi speech recognition with grammars that can be set active/inactive dynamically at decode-time
faster-whisper - Faster Whisper transcription with CTranslate2
monkeytype - The most customizable typing website with a minimalistic design and a ton of features. Test yourself in various modes, track your progress and improve your speed.