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"datePublished": "2023-06-03T05:34:04.000Z",
"name": "Nous-Hermes-13b",
"description": "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 compute, and several other contributors. 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. The fine-tuning process was performed with a 2000 sequence length on an 8x a100 80GB DGX machine for over 50 hours.",
"author": {
"name": "Nous Research",
"url": "https://nousresearch.com",
"blurb": "Nous Research is dedicated to advancing the field of natural language processing, in collaboration with the open-source community, through bleeding-edge research and a commitment to symbiotic development."
},
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"_descriptorVersion": "0.0.1",
"datePublished": "2023-07-22T02:20:14.000Z",
"name": "Nous-Hermes-Llama2-13b",
"description": "The Nous-Hermes-Llama2-13b is a state-of-the-art language model developed by Nous Research in collaboration with Teknium, Emozilla, and Redmond AI, fine-tuned on over 300,000 instructions. It uses the same dataset as its predecessor, Hermes on Llama-1, to maintain consistency while enhancing capability. This model is known for generating long responses with a lower hallucination rate, free from OpenAI censorship mechanisms. It was fine-tuned using a sequence length of 4096 on an 8x a100 80GB DGX machine. The training dataset is composed of synthetic GPT-4 outputs, including data from diverse sources such as GPTeacher, roleplay v1&2, code instruct datasets, and unpublished Nous Instruct & PDACTL, among others. The model utilizes the Alpaca prompt format for instructions and responses.",
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"name": "Nous Research",
"url": "https://nousresearch.com",
"blurb": "Nous Research is dedicated to advancing the field of natural language processing, in collaboration with the open-source community, through bleeding-edge research and a commitment to symbiotic development."
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},
{
"_descriptorVersion": "0.0.1",
"datePublished": "2023-12-11T06:26:58",
"name": "NexusRaven-V2-13B",
"description": "NexusRaven-V2 accepts a list of python functions. These python functions can do anything (e.g. sending GET/POST requests to external APIs). The two requirements include the python function signature and the appropriate docstring to generate the function call. *** Follow NexusRaven's prompting guide found on the model's Hugging Face page. ***",
"author": {
"name": "Nexusflow",
"url": "https://nexusflow.ai",
"blurb": "Nexusflow is democratizing Cyber Intelligence with Generative AI, fully on top of open-source large language models (LLMs)"
},
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},
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{
"_descriptorVersion": "0.0.1",
"datePublished": "2023-10-29T21:27:30",
"name": "OpenHermes 2.5 Mistral 7B",
"description": "OpenHermes 2.5 Mistral 7B is an advanced iteration of the OpenHermes 2 language model, enhanced by training on a significant proportion of code datasets. This additional training improved performance across several benchmarks, notably TruthfulQA, AGIEval, and the GPT4All suite, while slightly decreasing the BigBench score. Notably, the model's ability to handle code-related tasks, measured by the humaneval score, increased from 43% to 50.7%. The training data consisted of one million entries, primarily sourced from GPT-4 outputs and other high-quality open datasets. This data was rigorously filtered and standardized to the ShareGPT format and subsequently processed using ChatML by the axolotl tool.",
"author": {
"name": "Teknium",
"url": "https://twitter.com/Teknium1",
"blurb": "Creator of numerous chart topping fine-tunes and a Co-founder of NousResearch"
},
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{
"_descriptorVersion": "0.0.1",
"datePublished": "2023-06-25T11:37:35.000Z",
"name": "Airboros-13b-gpt4-1.4",
"description": "This qlora fine-tuned 13b parameter LlaMa model uses synthetic training data created via github.com/jondurbin/airoboros. It extends version 1.1, introducing thousands of new training data and an update for \"PLAINFORMAT\" to print code without backticks or explanations. The dataset, available online, focuses on coding, math/reasoning, trivia, role playing, multiple choice, fill-in-the-blank, context-obedient question answering, theory of mind, and general topics. The model was fine-tuned with a qlora fork, updated to use a modified vicuna template compatible with 7b/13b versions. The format involves a preamble/system prompt, followed by \"USER: [prompt] ASSISTANT: \", with the prompt allowing for multiple lines and spaces.",
"author": {
"name": "Jon Durbin",
"url": "https://github.com/jondurbin",
"blurb": "Jon Durbin is a Computer Scientist and the author of the Airboros (7B, 13B, 33B, 65B) qlora fine-tuned LlaMa family of models."
},
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