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dan su

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reacted to chansung's post with 👍 5 days ago
Simple Summarization on DeepSeek-R1 from DeepSeek AI The RL stage is very important. ↳ However, it is difficult to create a truly helpful AI for people solely through RL. ↳ So, we applied a learning pipeline consisting of four stages: providing a good starting point, reasoning RL, SFT, and safety RL, and achieved performance comparable to o1. ↳ Simply fine-tuning other open models with the data generated by R1-Zero (distillation) resulted in performance comparable to o1-mini. Of course, this is just a brief overview and may not be of much help. All models are accessible on Hugging Face, and the paper can be read through the GitHub repository. Model: https://huggingface.co/deepseek-ai Paper: https://github.com/deepseek-ai/DeepSeek-R1
reacted to danielhanchen's post with 🔥 18 days ago
Deepseek V3, including GGUF + bf16 versions are now uploaded! Includes 2, 3, 4, 5, 6 and 8-bit quantized versions. GGUFs: https://huggingface.co/unsloth/DeepSeek-V3-GGUF bf16: https://huggingface.co/unsloth/DeepSeek-V3-bf16 Min. hardware requirements to run: 48GB RAM + 250GB of disk space for 2-bit. See how to run them with examples and the full collection: https://huggingface.co/collections/unsloth/deepseek-v3-all-versions-677cf5cfd7df8b7815fc723c
reacted to reddgr's post with 👀 18 days ago
Major update on the Talking to Chatbots dataset! Expanded the 'wrapped' dataset (one row per chat) to 2.86k records, and the 'unwrapped' version (one row per conversation turn) to 11k records. The main source is my ChatGPT archive with nearly 2 years of chats. It is still a work in progress as I incorporate chats from other sources and qualitative metrics (SCBN) for responses. https://huggingface.co/datasets/reddgr/talking-to-chatbots-unwrapped-chats https://huggingface.co/datasets/reddgr/talking-to-chatbots-chats
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