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Large, very low quality loss - recommended Legacy medium, balanced quality - prefer using Q4_K_M

Legacy small, very high quality loss - prefer using Q3_K_M Smallest, significant quality loss - not recommended for most purposes Refer to the Provided Files table below to see what files use which methods, and how. Super-blocks with 16 blocks, each block having 16 weights. GGML_TYPE_Q6_K - 'type-0' 6-bit quantization.Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw GGML_TYPE_Q5_K - 'type-1' 5-bit quantization.Scales and mins are quantized with 6 bits. GGML_TYPE_Q4_K - 'type-1' 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights.GGML_TYPE_Q3_K - 'type-0' 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights.This ends up effectively using 2.5625 bits per weight (bpw) Block scales and mins are quantized with 4 bits. GGML_TYPE_Q2_K - 'type-1' 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight.They are also compatible with many third party UIs and libraries - please see the list at the top of this README. These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d36d5be95a0d9088b674dbb27354107221 If you don't know the answer to a question, please don't share false information. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. Please ensure that your responses are socially unbiased and positive in nature. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.

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Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions.

2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference.

GPTQ models for GPU inference, with multiple quantisation parameter options.

candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.

llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.

ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.

#Faraday.dev download the last version for iphone windows#

v, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.

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LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures.text-generation-webui, the most widely used web UI, with many features and powerful extensions.Here is an incomplate list of clients and libraries that are known to support GGUF: It is also supports metadata, and is designed to be extensible. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF is a new format introduced by the llama.cpp team on August 21st 2023. This repo contains GGUF format model files for Meta's Llama 2 13B-chat. TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)