serving
exllama
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serving | exllama | |
---|---|---|
12 | 64 | |
6,071 | 2,582 | |
0.2% | - | |
9.8 | 9.0 | |
3 days ago | 7 months ago | |
C++ | Python | |
Apache License 2.0 | 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.
serving
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Llama.cpp: Full CUDA GPU Acceleration
Yet another TEDIOUS BATTLE: Python vs. C++/C stack.
This project gained popularity due to the HIGH DEMAND for running large models with 1B+ parameters, like `llama`. Python dominates the interface and training ecosystem, but prior to llama.cpp, non-ML professionals showed little interest in a fast C++ interface library. While existing solutions like tensorflow-serving [1] in C++ were sufficiently fast with GPU support, llama.cpp took the initiative to optimize for CPU and trim unnecessary code, essentially code-golfing and sacrificing some algorithm correctness for improved performance, which isn't favored by "ML research".
NOTE: In my opinion, a true pioneer was DarkNet, which implemented the YOLO model series and significantly outperformed others [2]. Same trick basically like llama.cpp
[1] https://github.com/tensorflow/serving
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[D] How do OpenAI and other companies manage to have real-time inference on model with billions of parameters over an API?
I mean, probably - it's written in C++ https://github.com/tensorflow/serving
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Should I wait for the M2 Macbook Pro?
We’re looking into that solution at the moment, the issue I’m referring to is related to this https://github.com/tensorflow/serving/issues/1948 we’ll know if the plug-in approach works for our uses soon but haven’t started looking into implementing it yet
- TF Serving has been unavailable for 9 days so far due to outdated GPG key
- TF Serving has been unavailable for 8 days
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Would you use maturin for ML model serving?
Which ML framework do you use? Tensorflow has https://github.com/tensorflow/serving. You could also use the Rust bindings to load a saved model and expose it using one of the Rust HTTP servers. It doesn't matter whether you trained your model in Python as long as you export its saved model.
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Is LaMDA Sentient? – An Interview [pdf]
Most likely it's a model server running something like https://github.com/tensorflow/serving and if there isn't a lot of load, the resource could kill some of its tasks. I wouldn't imagine it's sitting around pondering deep thoughts.
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Ask HN: How to deploy a TensorFlow model for access through an HTTP endpoint?
https://github.com/tensorflow/serving
https://thenewstack.io/tutorial-deploying-tensorflow-models-...
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Popular Machine Learning Deployment Tools
GitHub
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If data science uses a lot of computational power, then why is python the most used programming language?
You serve models via https://www.tensorflow.org/tfx/guide/serving which is written entirely in C++ (https://github.com/tensorflow/serving/tree/master/tensorflow_serving/model_servers), no Python on the serving path or in the shipped product.
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?
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
MNN - MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
llama.cpp - LLM inference in C/C++
flashlight - A C++ standalone library for machine learning
GPTQ-for-LLaMa - 4 bits quantization of LLaMa using GPTQ
XLA.jl - Julia on TPUs
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
oneflow - OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
KoboldAI
glow - Compiler for Neural Network hardware accelerators
text-generation-inference - Large Language Model Text Generation Inference