llm-colosseum
tf-metal-experiments
llm-colosseum | tf-metal-experiments | |
---|---|---|
4 | 5 | |
942 | 265 | |
74.6% | - | |
9.4 | 0.0 | |
8 days ago | about 2 years ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | 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.
llm-colosseum
- LLM Colosseum
- Evaluate LLMs in Real Time with Street Fighter III
-
LLM Colosseum: Make LLMs fight in SFIII
Hello guys,
Tired of current boring LLMs benchmark ? I'm sharing with you a fun project built during the Mistral AI SF hackathon.
Using a RL framework, we made LLMs fight against each other in real time in Street Fighter III. You can find the repo here : https://github.com/OpenGenerativeAI/llm-colosseum.
Aside from the fact that it's very funny to see Mistral and others performing Hadouken, we found that it is a great way to benchmark language models. They need to quickly understand their environment and take actions accordingly.
With >400 fights, check out the ELO ranking on the HF space here : https://huggingface.co/spaces/junior-labs/llm-colosseum
tf-metal-experiments
- Launch HN: Metal (YC W23) – Embeddings as a Service
-
M2 Pro or M2 Max for AI?
My take on Apple M series SOCs: I don’t think any of them can hold a candle to Nvidia GPUs. The M2 Pro is like 1/8th of a 3090 and the M2 Max is 1/5th. https://github.com/tlkh/tf-metal-experiments
- TensorFlow Metal Back End on Apple Silicon Experiments (Just for Fun)
- [N] AMD launches MI200 AI accelerators (2.5x Nvidia A100 FP32 performance)
-
[D] How does tensorflow perform on M1 Pro/Max?
Some initial tests going on here: https://github.com/tlkh/tf-metal-experiments
What are some alternatives?
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
MetalPetal - A GPU accelerated image and video processing framework built on Metal.
HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision
adanet - Fast and flexible AutoML with learning guarantees.
ML-Workspace - 🛠 All-in-one web-based IDE specialized for machine learning and data science.
metal
MetalFilters - Instagram filters implemented in Metal
litenn - Lightweight machine learning library based on OpenCL 1.2
chatgpt-retrieval-plugin - The ChatGPT Retrieval Plugin lets you easily find personal or work documents by asking questions in natural language.