generate-text

A 32B experimental reasoning model for advanced text generation and robust instruction following. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>

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 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>

Generate novel text - novel finetuned from skt KoGPT2 base v2 - 한국어

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 quantized model fine-tuned for rapid, efficient, and robust conversational and instruction tasks. <metadata> gpu: A100 | collections: ["vLLM","AWQ"] </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 7B autoregressive language model by Mistral AI, optimized for efficient text generation and robust reasoning. <metadata> gpu: A100 | collections: ["vLLM"] </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>

Deploy GGUF quantized version of Tinyllama-1.1B GGUF vLLM for efficient inference. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>

A client-side mod that generates schematic files (.nbt) with shapes like spheres, rings, spirals, stars, and text.

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!

A chat model fine-tuned on TinyLlama, a compact 1.1B Llama model pretrained on 3 trillion tokens. <metadata> gpu: T4 | collections: ["vLLM"] </metadata>

2B instruct-tuned model for delivering coherent and instruction-following responses across a wide range of tasks. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>

An 8B-parameter, instruction-tuned variant of Meta's Llama-3.1 model, optimized in GGUF format for efficient inference. <metadata> gpu: A100 | collections: ["lama.cpp"] </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>

A robust 8B parameter base model for diverse language tasks, offering strong performance in multilingual scenarios. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>

24B instruction-tuned model, delivering context-aware, reliable responses optimized for performance and efficiency. <metadata> gpu: A100 | collections: ["vLLM"] </metadata>

35B model delivering high performance in reasoning, summarization, and question answering. <metadata> gpu: A100 | collections: ["HF Transformers"] </metadata>

A medical LLM built on LLaMA-3.1-70B, employing detailed step-by-step reasoning for complex medical problem-solving. <metadata> gpu: A100 | collections: ["HF Transformers","Variable Inputs"] </metadata>