5. Papers with Code Papers with Code consist of more than 7000 Public Datasets on different modalities.
OPEN SOURCE
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Open ML: Access 20K+ Datasets for Machine Learning
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3. Open ML Find 20K+ datasets on Open ML https://
openml.org -
Awesome Data Github Repository with Public Datasets
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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.
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llamafile: Running LLMs Locally Without Docker
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llamafile is pretty much that but without the Docker dependency
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Mistral Releases Mixtral 8×22B Frontier LLM Model
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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.
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MacBook Pro RAM Constraints for Running Mixtral 8x22B
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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
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New llm-gemini Plugin Release with Embedding Model Support
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Exactly what I needed, thank you! I just released a new llm-gemini plugin version with support for that embedding model: https://
github.com/simonw/llm-gem
ini/releases/tag/0.1a3
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GPT-2 as Ideal Class Project for Educational Book
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GPT-2 turns out to be a perfect class project for that book (which I really like and own)
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Explaining LLM.c: Simplifying Large Language Model Training
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# explaining llm.c in layman terms Training Large Language Models (LLMs), like ChatGPT, involves a large amount of code and complexity. For example, a typical LLM training project might use the PyTorch deep learning library. PyTorch is quite complex because it implements a very https://
t.co/dkpZGRvkmU -
Mixtral-8x22B Fine-tuning Configuration with LoRA Parameters
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base_model: mistral-community/Mixtral-8x22B-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true load_in_8bit: false
load_in_4bit: false
strict: false unfrozen_parameters: – ^lm_head.weight$ – ^model.embed_tokens.weight$ –