Is MLX the best Fine Tuning Framework?
🚀 Want to fine-tune AI models on your Mac without cloud services? As an ex-Ollama developer, I'll show you how to use Apple's MLX framework to fine-tune mod...
Blaizzy/mlx-vlm: MLX-VLM is a package for running Vision LLMs locally on your Mac using MLX.
MLX-VLM is a package for running Vision LLMs locally on your Mac using MLX. - Blaizzy/mlx-vlm
Creating a LLM-as-a-Judge that drives business results
Hamel Husain's sequel to [Your AI product needs evals](https://hamel.dev/blog/posts/evals/). This is _packed_ with hard-won actionable advice. Hamel warns against using scores on a 1-5 scale, instead promoting an alternative he …
What We Learned from a Year of Building with LLMs (Part I)
RAFT: A new way to teach LLMs to be better at RAG
In this article, we will look at the limitations of RAG and domain-specific Fine-tuning to adapt LLMs to existing knowledge and how a team of UC Berkeley..
WeihaoTan/TWOSOME: Implementation of TWOSOME
flowersteam/Grounding_LLMs_with_online_RL: We perform functional grounding of LLMs' knowledge in BabyAI-Text
KhoomeiK/LlamaGym: Fine-tune LLM agents with online reinforcement learning
Fine-tune LLM agents with online reinforcement learning - KhoomeiK/LlamaGym
Finetuning Open-Source LLMs
This video offers a quick dive into the world of finetuning Large Language Models (LLMs). This video covers - common usage scenarios for pretrained LLMs- par...
Fast Llama 2 on CPUs With Sparse Fine-Tuning and DeepSparse - Neural Magic
Key Takeaways We expanded our Sparse Fine-Tuning research results to include Llama 2. The results include 60% sparsity with INT8 quantization and no drop in accuracy. DeepSparse now supports accelerated inference of sparse-quantized Llama 2 models, with inference speeds 6-8x faster over the baseline at 60-80% sparsity. We used some interesting algorithmic techniques in order
jondurbin/airoboros: Customizable implementation of the self-instruct paper.