AgileRL
mlc-llm
| AgileRL | mlc-llm | |
|---|---|---|
| 12 | 89 | |
| 501 | 17,215 | |
| 4.2% | 4.6% | |
| 9.8 | 9.9 | |
| 5 days ago | 3 days ago | |
| Python | Python | |
| Apache License 2.0 | 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.
AgileRL
- [P] Introducing PPO and Rainbow DQN to our super fast evolutionary HPO reinforcement learning framework
- Introducing PPO and Rainbow DQN to our super fast evolutionary HPO reinforcement learning framework
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[P] Significant improvements for multi-agent reinforcement learning!
Please check it out! https://github.com/AgileRL/AgileRL
- 10x faster reinforcement learning hyperparameter optimization than SOTA - now with distributed training!
- [P] 10x faster reinforcement learning hyperparameter optimization than SOTA - now with distributed training!
-
(1/2) May 2023
Deep Reinforcement Learning library focused on improving development by introducing RLOps - MLOps for reinforcement learning (https://github.com/AgileRL/AgileRL)
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[P] 10x faster reinforcement learning HPO - now for RLHF!
https://github.com/AgileRL/AgileRL/blob/main/CONTRIBUTING.md Has a link to our discord too
- 10x faster reinforcement learning HPO - now with CNNs!
- [P] 10x faster reinforcement learning HPO - now with CNNs!
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[P] Reinforcement learning evolutionary hyperparameter optimization - 10x speed up
GitHub: https://github.com/AgileRL/AgileRL
mlc-llm
- FLaNK 04 March 2024
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Ai on a android phone?
This one uses gpu, it doesn't support Mistral yet: https://github.com/mlc-ai/mlc-llm
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MLC vs llama.cpp
I have tried running mistral 7B with MLC on my m1 metal. And it kept crushing (git issue with description). Memory inefficiency problems.
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[Project] Scaling LLama2 70B with Multi NVIDIA and AMD GPUs under 3k budget
Project: https://github.com/mlc-ai/mlc-llm
- Scaling LLama2-70B with Multi Nvidia/AMD GPU
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AMD May Get Across the CUDA Moat
For LLM inference, a shoutout to MLC LLM, which runs LLM models on basically any API that's widely available: https://github.com/mlc-ai/mlc-llm
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ROCm Is AMD's #1 Priority, Executive Says
One of your problems might be that gfx1032 is not supported by AMD's ROCm packages, which has a laughably short list of supported hardware: https://rocm.docs.amd.com/en/latest/release/gpu_os_support.h...
The normal workaround is to assign the closest architecture, eg gfx1030, so `HSA_OVERRIDE_GFX_VERSION=10.3.0` might help
Also, it looks like some of your tested projects are OpenCL? For me, I do something like: `yay -S rocm-hip-sdk rocm-ml-sdk rocm-opencl-sdk` to cover all the bases.
My recent interest has been LLMs and this is my general step by step for those (llama.cpp, exllama) for those interested: https://llm-tracker.info/books/howto-guides/page/amd-gpus
I didn't port the docs back in, but also here's a step-by-step w/ my adventures getting TVM/MLC working w/ an APU: https://github.com/mlc-ai/mlc-llm/issues/787
From my experience, ROCm is improving, but there's a good reason that Nvidia has 90% market share even at big price premiums.
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Show HN: Ollama for Linux – Run LLMs on Linux with GPU Acceleration
Maybe they're talking about https://github.com/mlc-ai/mlc-llm which is used for web-llm (https://github.com/mlc-ai/web-llm)? Seems to be using TVM.
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Show HN: Fine-tune your own Llama 2 to replace GPT-3.5/4
you already have TVM for the cross platform stuff
see https://tvm.apache.org/docs/how_to/deploy/android.html
or https://octoml.ai/blog/using-swift-and-apache-tvm-to-develop...
or https://github.com/mlc-ai/mlc-llm
- Ask HN: Are you training and running custom LLMs and how are you doing it?
What are some alternatives?
chat-ui - Open source codebase powering the HuggingChat app
llama.cpp - LLM inference in C/C++
RLeXplore - RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
ggml - Tensor library for machine learning
loopquest - A Production Tool for Embodied AI
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
de-torch - Minimal PyTorch Library for Differential Evolution
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
Muzero - Pytorch Implementation of MuZero for gym environment. It support any Discrete , Box and Box2D configuration for the action space and observation space.
llama-cpp-python - Python bindings for llama.cpp
q-learning-algorithms - This repository will aim to provide implementations of q-learning algorithms (DQN, Double-DQN, ...) using Pytorch.
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.