ArchGPT
lmdeploy
ArchGPT | lmdeploy | |
---|---|---|
1 | 4 | |
12 | 2,781 | |
- | 16.4% | |
7.8 | 9.8 | |
5 months ago | 7 days ago | |
TypeScript | Python | |
MIT License | Apache License 2.0 |
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.
ArchGPT
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Meta: Code Llama, an AI Tool for Coding
Iâm working on a project related to that: https://github.com/0a-io/Arch-GPT
I think hypergraph is an overlooked concept in programming language theory
lmdeploy
- FLaNK-AIM Weekly 06 May 2024
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AMD May Get Across the CUDA Moat
I wouldnât say ROCm code is âslowerâ, per se, but in practice thatâs how it presents. References:
https://github.com/InternLM/lmdeploy
https://github.com/vllm-project/vllm
https://github.com/OpenNMT/CTranslate2
You know whatâs missing from all of these and many more like them? Support for ROCm. This is all before you get to the really wildly performant stuff like Triton Inference Server, FasterTransformer, TensorRT-LLM, etc.
ROCm is at the âget it to work stageâ (see top comment, blog posts everywhere celebrating minor successes, etc). CUDA is at the âwring every last penny of performance out of this thingâ stage.
In terms of hardware support, I think that one is obvious. The U in CUDA originally stood for unified. Look at the list of chips supported by Nvidia drivers and CUDA releases. Literally anything from at least the past 10 years that has Nvidia printed on the box will just run CUDA code.
One of my projects specifically targets Pascal up - when I thought even Pascal was a stretch. Cue my surprise when I got a report of someone casually firing it up on Maxwell when I was pretty certain there was no way it could work.
A Maxwell laptop chip. It also runs just as well on an H100.
THAT is hardware support.
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Nvidia Introduces TensorRT-LLM for Accelerating LLM Inference on H100/A100 GPUs
vLLM has healthy competition. Not affiliated but try lmdeploy:
https://github.com/InternLM/lmdeploy
In my testing itâs significantly faster and more memory efficient than vLLM when configured with AWQ int4 and int8 KV cache.
If you look at the PRs, issues, etc youâll see there are many more optimizations in the works. That said there are also PRs and issues for some of the lmdeploy tricks in vllm as well (AWQ, Triton Inference Server, etc).
Iâm really excited to see where these projects go!
- Meta: Code Llama, an AI Tool for Coding
What are some alternatives?
smartcat
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
llama.cpp - LLM inference in C/C++
codellama - Inference code for CodeLlama models
llama-cpp-python - Python bindings for llama.cpp
ggml - Tensor library for machine learning
CTranslate2 - Fast inference engine for Transformer models
ollama-ui - Simple HTML UI for Ollama
seamless_communication - Foundational Models for State-of-the-Art Speech and Text Translation
refact - WebUI for Fine-Tuning and Self-hosting of Open-Source Large Language Models for Coding