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Smaug-72B topped the Hugging Face LLM leaderboard and it’s the first model with an average score of 80, making it the world’s best open-source foundation model. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>
A 32B experimental reasoning model for advanced text generation and robust instruction following. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>
A distilled DeepSeek-R1 variant built on Qwen2.5-32B, fine-tuned with curated data for enhanced performance and efficiency. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>
A variant of the BART model designed specifically for natural language summarization. It was pre-trained on a large corpus of English text and later fine-tuned on the CNN/Daily Mail dataset. <metadata> gpu: T4 | collections: ["HF Transformers"] </metadata>
A GPTQ‑quantized version of Eric Hartford’s Dolphin 2.5 Mixtral 8x7B model, fine‑tuned for coding and conversational tasks. <metadata> gpu: A100 | collections: ["vLLM","GPTQ"] </metadata>
A GPTQ-quantized variant of the Mixtral 8x7B model, fine-tuned for efficient text generation and conversational applications. <metadata> gpu: A100 | collections: ["vLLM","GPTQ"] </metadata>
A quantized model fine-tuned for rapid, efficient, and robust conversational and instruction tasks. <metadata> gpu: A100 | collections: ["vLLM","AWQ"] </metadata>
A 7B autoregressive language model by Mistral AI, optimized for efficient text generation and robust reasoning. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>
In this notebook, I'll construct a character-level LSTM with PyTorch. The network will train character by character on some text, then generate new text character by character. As an example, I will train on Anna Karenina. This model will be able to generate new text based on the text from the book!
2B instruct-tuned model for delivering coherent and instruction-following responses across a wide range of tasks. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>
A robust 8B parameter base model for diverse language tasks, offering strong performance in multilingual scenarios. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>
35B model delivering high performance in reasoning, summarization, and question answering. <metadata> gpu: A100 | collections: ["HF Transformers"] </metadata>
Advanced multimodal language model developed by Mistral AI with enhanced text performance, robust vision capabilities, and an expanded context window of up to 128,000 tokens. <metadata> gpu: A100 | collections: ["HF Transformers"] </metadata>