diff --git a/catalog.json b/catalog.json index 3ac4498..6f60ef5 100644 --- a/catalog.json +++ b/catalog.json @@ -1,9 +1,67 @@ [ + { + "_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" + }, + "numParameters": "7B", + "resources": { + "canonicalUrl": "https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B", + "downloadUrl": "https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF" + }, + "trainedFor": "chat", + "arch": "mistral", + "files": { + "highlighted": { + "economical": { + "name": "openhermes-2.5-mistral-7b.Q4_K_S.gguf" + }, + "most_capable": { + "name": "openhermes-2.5-mistral-7b.Q6_K.gguf" + } + }, + "all": [ + { + "name": "openhermes-2.5-mistral-7b.Q4_K_S.gguf", + "url": "https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/resolve/main/openhermes-2.5-mistral-7b.Q4_K_S.gguf", + "sizeBytes": 4140385024, + "quantization": "Q4_K_S", + "format": "gguf", + "sha256checksum": "5ae9c3c11ce520a2360dcfca1f4e38392dc0b7a49413ce6695857a5148a71d35", + "publisher": { + "name": "TheBloke", + "socialUrl": "https://twitter.com/TheBlokeAI" + }, + "respository": "TheBloke/OpenHermes-2.5-Mistral-7B-GGUF", + "repositoryUrl": "https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF" + }, + { + "name": "openhermes-2.5-mistral-7b.Q6_K.gguf", + "url": "https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF/resolve/main/openhermes-2.5-mistral-7b.Q6_K.gguf", + "sizeBytes": 5942078272, + "quantization": "Q6_K", + "format": "gguf", + "sha256checksum": "cd4caa42229e973636e9d4c8db50a89593353c521e0342ca615756ded2b977a2", + "publisher": { + "name": "TheBloke", + "socialUrl": "https://twitter.com/TheBlokeAI" + }, + "respository": "TheBloke/OpenHermes-2.5-Mistral-7B-GGUF", + "repositoryUrl": "https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF" + } + ] + } + }, { "_descriptorVersion": "0.0.1", "datePublished": "2024-02-21T16:54:57.000Z", "name": "Google's Gemma 2B Instruct", - "description": "** Requires LM Studio 0.2.15 or newer ** Gemma is a family of lightweight LLMs built from the same research and technology Google used to create the Gemini models. Gemma models are available in two sizes, 2 billion and 7 billion parameters. These models are trained on up to 6T tokens of primarily English web documents, mathematics, and code, using a transformer architecture with enhancements like Multi-Query Attention, RoPE Embeddings, GeGLU Activations, and advanced normalization techniques.", + "description": "Gemma is a family of lightweight LLMs built from the same research and technology Google used to create the Gemini models. Gemma models are available in two sizes, 2 billion and 7 billion parameters. These models are trained on up to 6T tokens of primarily English web documents, mathematics, and code, using a transformer architecture with enhancements like Multi-Query Attention, RoPE Embeddings, GeGLU Activations, and advanced normalization techniques.", "author": { "name": "Google DeepMind", "url": "https://deepmind.google", @@ -43,117 +101,84 @@ }, { "_descriptorVersion": "0.0.1", - "datePublished": "2023-12-12T10:12:59", - "name": "Mistral 7B Instruct v0.2", - "description": "The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of Mistral-7B-Instruct-v0.1. For full details of this model read MistralAI's blog post and paper.", + "datePublished": "2024-03-20T00:31:49.000Z", + "name": "Stable Code Instruct 3B ", + "description": "Stable Code Instruct 3B is a decoder-only language model with 2.7 billion parameters, developed from the stable-code-3b. 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It is a fine-tuned version of the mistralai/Mistral-7B-v0.1 model, leveraging a 7B parameter GPT-like architecture. The model has been trained on a combination of synthetic datasets and publicly available data using Direct Preference Optimization (DPO), a technique that improved its performance on the MT Bench. An important aspect to note is that the in-built alignment of the training datasets was deliberately omitted during the training process, a decision that, while enhancing the model's helpfulness, also makes it prone to generating potentially problematic outputs when prompted. Therefore, it is advised to use the model strictly for research and educational purposes. The model primarily supports the English language and is licensed under the MIT License. Additional details can be found in the associated technical report.", + "datePublished": "2023-09-27T16:12:57", + "name": "Mistral 7B Instruct v0.1", + "description": "The Mistral-7B-Instruct-v0.1 is a Large Language Model (LLM) developed by Mistral AI. This LLM is an instruct fine-tuned version of a generative text model, leveraging a variety of publicly available conversation datasets. The model's architecture is based on a transformer model, featuring Grouped-Query Attention, Sliding-Window Attention, and a Byte-fallback BPE tokenizer. To utilize the instruction fine-tuning capabilities, prompts should be enclosed within [INST] and [/INST] tokens. The initial instruction should commence with a beginning-of-sentence id, whereas subsequent instructions should not. The generation process by the assistant will terminate with the end-of-sentence token id. 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