DeepSeek-Coder VS cuml

deepseek-coder-instruct 6.7B still looks like is better than llama 3 8B on HumanEval [0], and deepseek-coder-instruct 33B still within reach to run on 32 GB Macbook M2 Max - Lamma 3 70B on the other hand will be hard to run locally unless you really have 128GB ram or more. But we will see in the following days how it performs in real life.

[0] https://github.com/deepseek-ai/deepseek-coder?tab=readme-ov-...

Deepseek (https://github.com/deepseek-ai/DeepSeek-Coder?tab=readme-ov-...) code is MIT and the model license is available too.

https://github.com/deepseek-ai/deepseek-coder

33B Instruct doesn’t beat 6.7B Instruct by much but maybe those % improvements mean more for your usage.

I run 6.7B since I have 16GB RAM.

Deepseek Coder: https://github.com/deepseek-ai/DeepSeek-Coder (Best open source coding model right now)

sklearn can't, bit take a look at cuML (https://github.com/rapidsai/cuml ). It uses the same API as sklearn but executes on GPU.

As a companion to the other comments, I'd like to mention that the RAPIDS library cuML provides GPU-accelerated versions of quite a few of the algorithms mentioned in this thread (HDBSCAN, UMAP, SVM, PCA, {Exact, Approximate} Nearest Neighbors, DBSCAN, KMeans, etc.).

CuML

Did they implement the clustering algorithm themselves? cuML is a GPU-accelerated scikit-learn-like package that covers many of the common ML algorithms.

https://github.com/rapidsai/cuml

> cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn. For large datasets, these GPU-based implementations can complete 10-50x faster than their CPU equivalents. For details on performance, see the cuML Benchmarks Notebook.

Cython code

Take a look at RAPIDS CUML https://github.com/rapidsai/cuml. It's useful for most common ML algorithms. Feel free to create Github issues for feature requests & bugs.