Brief-details: InternVL2_5-1B-MPO is a 1B-parameter multimodal LLM with enhanced reasoning capabilities through Mixed Preference Optimization, supporting vision-language tasks with dynamic resolution.
BRIEF DETAILS: 4-bit quantized 32B parameter Qwen model distilled from DeepSeek-R1, optimized for reasoning tasks with strong math and coding capabilities.
Brief-details: A debugging-focused speech recognition model based on Whisper-v3, randomly initialized with reduced architecture size for testing and development purposes
BRIEF-DETAILS: StableBeluga-13B: A Llama2-based 13B parameter LLM fine-tuned on Orca-style dataset. Optimized for instruction following and safe AI interactions.
BRIEF-DETAILS: 12B parameter GGUF quantized model with multiple compression variants, optimized for human-like interactions. Features Q2-Q8 quantization options and fast inference capabilities.
BRIEF-DETAILS: A comprehensive guide to Postman alternatives for API development & testing, featuring 8 modern tools with detailed comparisons and feature analysis.
BRIEF-DETAILS: A quantized version of Josiefied-Qwen2.5-14B offering multiple compression options, optimized for efficient deployment with sizes ranging from 5.9GB to 15.8GB.
BRIEF-DETAILS: Turkish-based Llama model fine-tuned for therapy applications, featuring 8B parameters and optimized training using Unsloth and TRL library.
Brief-details: A sophisticated 14B parameter merged LLM combining Qwen2.5 and Lamarck models, optimized using Model Stock merge method with bfloat16 precision and specialized instruction tuning.
BRIEF-DETAILS: 12B parameter GGUF quantized model with multiple compression variants (Q2-Q8), offering flexibility between size (4.9-13.1GB) and quality tradeoffs.
Brief-details: An uncensored variant of FluentlyLM-Prinum using abliteration technique, designed for unrestricted text generation and available through Ollama integration.
Brief-details: GGUF quantized version of Blagoveshchensk 14B V3 with multiple compression options (Q2-Q8), optimized for efficient local deployment and inference
BRIEF DETAILS: 12B parameter GGUF quantized model with multiple compression variants (3.1GB-10.2GB), optimized for different performance/quality tradeoffs
Brief-details: A 12B parameter GGUF-quantized language model offering multiple compression variants (Q2-Q8) with size options ranging from 4.9GB to 13.1GB, optimized for different performance/quality trade-offs
Brief Details: GGUF quantized version of Nemo-12b-Humanize offering multiple compression variants (3.1GB-10.2GB). Optimized for efficient deployment with various quality-performance tradeoffs.
BRIEF-DETAILS: Preview release of Qwen2.5's 14B parameter YOYO series, featuring enhanced performance and planned 1M token context support
Brief Details: A sophisticated 14B parameter merge model combining 7 high-quality language models, built on Qwen2.5 architecture using Model Stock merge method.
BRIEF-DETAILS: A sophisticated 14B parameter LLM merging Lamarck, Chocolatine, and Qwenvergence models using SLERP method with dual-slice architecture for enhanced stability and eloquence.
Brief-details: Nemo-12b weights with imatrix quantization offering various GGUF formats from 3.1GB to 10.2GB. Optimized for efficient deployment with quality/size tradeoffs.
Brief-details: 12B parameter GGUF-formatted language model with multiple quantization options, optimized for efficiency and human-like responses, featuring various compression levels from Q2 to Q8
BRIEF-DETAILS: 8B parameter merged LLM combining Forgotten-Safeword and Ministrations models using DARE TIES method with 0.3 density and equal weights
