nous hermes 13b ggml | TheBloke/Nous nous hermes 13b ggml Nous-Hermes-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the .
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Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. See moreThe new methods available are: 1. GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) 2. . See more
I use the following command line; adjust for your tastes and needs: Change -t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use -t 8. Change -ngl 32to the number of layers to offload to GPU. Remove it if . See moreNous-Hermes-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine .These files are GGML format model files for NousResearch's Nous-Hermes-13B. GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as: text-generation-webui. KoboldCpp.Nous-Hermes-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the .
Nous-Hermes-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the . In my own (very informal) testing I've found it to be a better all-rounder and make less mistakes than my previous favorites, which include airoboros, wizardlm 1.0, vicuna 1.1, and a few of their variants. Find ggml/gptq/etc versions here: https://huggingface.co/models?search=nous-hermes. Add a Comment.
So for now, I'll use Nous Hermes Llama2 as my current main model, replacing my previous LLaMA (1) favorites Guanaco and Airoboros. Those were 33Bs, but in my comparisons with them, the Llama 2 13Bs are just as good and equivalent to . A ggml and gptq quantized model will be available soon. This can then be loaded on llama.cpp or oobabooga web ui for people with less vram and ram. Explore the list of Nous-Hermes model variations, their file formats (GGML, GGUF, GPTQ, and HF), and understand the hardware requirements for local inference.GPTQ models for GPU inference, with multiple quantisation parameter options. 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference. NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions.
The result is an enhanced Llama 13b model that rivals GPT-3.5-turbo in performance across a variety of tasks. This model stands out for its long responses, low hallucination rate, and absence of OpenAI censorship mechanisms. I've settled on Chronolima-Airo-Grad-L2-13B-GGML after everything and I have been using it for a bit now. I am extremely happy with it compared to llama2 nous Hermes and the new Chronos Hermes llama 2..
These files are GGML format model files for NousResearch's Nous-Hermes-13B. GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as: text-generation-webui. KoboldCpp.Nous-Hermes-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the .Nous-Hermes-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the .
In my own (very informal) testing I've found it to be a better all-rounder and make less mistakes than my previous favorites, which include airoboros, wizardlm 1.0, vicuna 1.1, and a few of their variants. Find ggml/gptq/etc versions here: https://huggingface.co/models?search=nous-hermes. Add a Comment.
So for now, I'll use Nous Hermes Llama2 as my current main model, replacing my previous LLaMA (1) favorites Guanaco and Airoboros. Those were 33Bs, but in my comparisons with them, the Llama 2 13Bs are just as good and equivalent to . A ggml and gptq quantized model will be available soon. This can then be loaded on llama.cpp or oobabooga web ui for people with less vram and ram. Explore the list of Nous-Hermes model variations, their file formats (GGML, GGUF, GPTQ, and HF), and understand the hardware requirements for local inference.
GPTQ models for GPU inference, with multiple quantisation parameter options. 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference. NousResearch's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions. The result is an enhanced Llama 13b model that rivals GPT-3.5-turbo in performance across a variety of tasks. This model stands out for its long responses, low hallucination rate, and absence of OpenAI censorship mechanisms.
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nous hermes 13b ggml|TheBloke/Nous
