#exllamav2 search results

Fun with grounding in Qwen2-VL. Finding the things. #wherearethethings #exllamav2 #cat


In the top menu, to the right of "Select a model" there is a gear icon. It will bring up the Settings modal. Select Connections and it will have a OpenAI API section. Add the http://ip:port/v1 of your tabbyAPI and your API key. That's it. #exllamav2 #exl2 #llm #localLlama


#exllamav2 #Python A fast inference library for running LLMs locally on modern consumer-class GPUs gtrending.top/content/3391/


#EXL2 #quantization format introduced in #ExLlamaV2 supports 2 to 8-bit precision. High performance on consumer GPUs. Mixed precision, smaller model size, and lower perplexity while maintaining accuracy. Find EXL2 models at llm.extractum.io/list/?exl2 #MachineLearning #EXL2 #LLMs

extractum_io's tweet image. #EXL2 #quantization format introduced in #ExLlamaV2 supports 2 to 8-bit precision. High performance on consumer GPUs. Mixed precision, smaller model size, and lower perplexity while maintaining accuracy. Find EXL2 models at llm.extractum.io/list/?exl2 
#MachineLearning #EXL2 #LLMs

#ExllamaV2 is currently the fastest inference framework for Mixtral 8x7 MoE. It is so good. Can run Mixtral 4bit GPTQ in a 24G + 8G GPU, 3 bit in just one 24G GPU. Its auto VRAM split loading is amazing. github.com/turboderp/exll…


Fun with grounding in Qwen2-VL. Finding the things. #wherearethethings #exllamav2 #cat


#ExllamaV2 is currently the fastest inference framework for Mixtral 8x7 MoE. It is so good. Can run Mixtral 4bit GPTQ in a 24G + 8G GPU, 3 bit in just one 24G GPU. Its auto VRAM split loading is amazing. github.com/turboderp/exll…


#EXL2 #quantization format introduced in #ExLlamaV2 supports 2 to 8-bit precision. High performance on consumer GPUs. Mixed precision, smaller model size, and lower perplexity while maintaining accuracy. Find EXL2 models at llm.extractum.io/list/?exl2 #MachineLearning #EXL2 #LLMs

extractum_io's tweet image. #EXL2 #quantization format introduced in #ExLlamaV2 supports 2 to 8-bit precision. High performance on consumer GPUs. Mixed precision, smaller model size, and lower perplexity while maintaining accuracy. Find EXL2 models at llm.extractum.io/list/?exl2 
#MachineLearning #EXL2 #LLMs

#exllamav2 #Python A fast inference library for running LLMs locally on modern consumer-class GPUs gtrending.top/content/3391/


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#EXL2 #quantization format introduced in #ExLlamaV2 supports 2 to 8-bit precision. High performance on consumer GPUs. Mixed precision, smaller model size, and lower perplexity while maintaining accuracy. Find EXL2 models at llm.extractum.io/list/?exl2 #MachineLearning #EXL2 #LLMs

extractum_io's tweet image. #EXL2 #quantization format introduced in #ExLlamaV2 supports 2 to 8-bit precision. High performance on consumer GPUs. Mixed precision, smaller model size, and lower perplexity while maintaining accuracy. Find EXL2 models at llm.extractum.io/list/?exl2 
#MachineLearning #EXL2 #LLMs

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