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>

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

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 quantized model fine-tuned for rapid, efficient, and robust conversational and instruction tasks. <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!

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>

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>

Implementing Hidden MarkovModel to generate new text and complete sentences

Shopify AI blogger. Generate blog posts with ChatGPT!

A 7B parameter model fine-tuned for dialogue, utilizing supervised learning and RLHF, supports a context length of up to 4,000 tokens. <metadata> gpu: A10 | collections: ["HF Transformers"] </metadata>

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

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

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

Import any model using TGI on Inferless using Dockerfile for streamlined containerized deployment. <metadata> collections: ["Dockerfile"] </metadata>

A GPTQ‑quantized 7B language model based on Mistral, fine‑tuned for robust, efficient conversational and text generation tasks. <metadata> gpu: A100 | collections: ["vLLM","GPTQ"] </metadata>

Quantized GGUF model which dramatically reduces memory requirements while preserving conversational quality. <metadata> gpu: A100 | collections: ["Using NFS Volumes", "llama.cpp"] </metadata>

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