Fine-tuned Mixtral 8x22B is ready. Curious what the easiest way to do inference is? Spinning up some GPUs for vLLM right now, is there something better?
OPEN SOURCE
-
HashiCorp Accused of Copying MPL-2.0 Licensed Code
By
–
"The code in question can be clearly shown to have been copied from older code under the MPL-2.0 license. HashiCorp seems to have copied the same code itself when they implemented their version of this feature." Pretty convincing summary right there.
-
Hugging Face: Access 80K+ Datasets for Machine Learning
By
–
7. Hugging Face You can find 80K+ datasets on Hugging Face. https://
huggingface.co/datasets -
Papers with Code: Over 7000 Public Datasets Available
By
–
5. Papers with Code Papers with Code consist of more than 7000 Public Datasets on different modalities.
-
Open ML: Access 20K+ Datasets for Machine Learning
By
–
3. Open ML Find 20K+ datasets on Open ML https://
openml.org -
Awesome Data Github Repository with Public Datasets
By
–
1. Awesome Data Github Repository In this repository, you can find direct links to all the Public datasets, and you can find all sorts of datasets like Images, Text, Audio, and Tabular.
-
llamafile: Running LLMs Locally Without Docker
By
–
llamafile is pretty much that but without the Docker dependency
-
Mistral Releases Mixtral 8×22B Frontier LLM Model
By
–
French AI startup Mistral released Mixtral 8×22B, a powerful new frontier LLM, dropped quietly via a 281GB file on X available for download. The LLM features a 65,000-token context window, 176B parameters and is expected to surpass the previous Mixtral.
-
MacBook Pro RAM Constraints for Running Mixtral 8x22B
By
–
Increasingly regretting only getting 64GB of RAM for my MacBook Pro M2 – it looks like Mixtral 8x22B needs 128GB
— Simon Willison (@simonw) 11 avril 2024
Hoping there will be quantized versions I can run soon https://t.co/PG9IqiBLewIncreasingly regretting only getting 64GB of RAM for my MacBook Pro M2 – it looks like Mixtral 8x22B needs 128GB Hoping there will be quantized versions I can run soon