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WTF do people use Open Models for??

Feb 22, 2025 · 28:01

Eugene Cheah of Featherless.ai breaks down how individuals and enterprises actually use open-source AI models, based on platform data. DeepSeek R1 dominates individual usage, but Mistral Nemo 8B remains the top enterprise model due to production stickiness and Apache 2.0 licensing. Creative writing and roleplay account for 30–40% of all traffic, with over 60% of users in that segment being women; coding copilots and agents make up 20–30%, driven by 'vibe coding' and token-hungry workflows like Kline. RAG and ChatGPT clones represent 20%, while agentic workflows (10–20%) succeed with human-in-the-loop designs. Cheah advises enterprises to aim for 80% automation with escape hatches, and warns against chasing 100% reliability. He concludes by introducing Quirky, a post-transformer hybrid built for $100k.

Decoding the Decoder LLM without de code: Ishan Anand

Aug 9, 2024 · 17:08

Ishan Anand demonstrates how decoder-based LLMs like GPT-2 work internally using a fully functional spreadsheet implementation. He explains tokenization (e.g., 're-injury' splitting into 'rain' and 'injury' due to subword units), 768-dimensional embeddings, multi-headed attention where tokens look backward (e.g., 'he' focuses 0.48 on 'Mike'), and the multi-layer perceptron with matrix multiplies (MMULT). He shows residual connections creating an information superhighway and uses logit lens to reveal layer-by-layer predictions (e.g., 'Wednesday' appears early but only rises to top probability at later layers). Finally, he injects a sparse autoencoder feature vector for 'Jedi' into the residual stream, steering GPT-2 to output 'lightsaber' instead of 'phone', and contrasts this with alternative steering methods like representation engineering and activation steering.