catalog.json

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        "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.",
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            "name": "Google DeepMind",
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        "name": "CodeLlama 7B Instruct",
        "description": "MetaAI has released Code Llama, a comprehensive family of large language models for code. These models are based on Llama 2 and exhibit state-of-the-art performance among openly available models. They offer advanced infilling capabilities, can accommodate large input contexts, and have the ability to follow instructions for programming tasks without prior training. There are various versions available to cater to a wide array of applications: foundation models (Code Llama), Python-specific models (Code Llama - Python), and models for following instructions (Code Llama - Instruct). These versions come with 7B, 13B, and 34B parameters respectively. All models are trained on 16k token sequences and show improvements even on inputs with up to 100k tokens. The 7B and 13B models of Code Llama and Code Llama - Instruct have the ability to infill based on surrounding content. In terms of performance, Code Llama has set new standards among open models on several code benchmarks, achieving scores of up to 53% on HumanEval and 55% on MBPP. Notably, the Python version of Code Llama 7B surpasses the performance of Llama 2 70B on HumanEval and MBPP. All of MetaAI's models outperform every other publicly available model on MultiPL-E. Code Llama has been released under a permissive license that enables both research and commercial use.",
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        "description": "The Zephyr-7B-\u03b2 is the second model in the Zephyr series, designed to function as an assistant. 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.",
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            "name": "Hugging Face H4",
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        "name": "StableLM Zephyr 3B",
        "description": "StableLM Zephyr 3B is an English-language, auto-regressive language model with 3 billion parameters, developed by Stability AI. It's an instruction-tuned model influenced by HuggingFace's Zephyr 7B training approach and is built on transformer decoder architecture. It was trained using a mix of public and synthetic datasets, including SFT and Preference Datasets from the HuggingFace Hub with Direct Preference Optimization (DPO). Its performance has been evaluated using the MT Bench and Alpaca Benchmark, achieving a score of 6.64 and a win rate of 76% respectively. For fine-tuning, it utilizes the StabilityAI's stablelm-3b-4e1t model and is available under the StabilityAI Non-Commercial Research Community License. Commercial use requires contacting Stability AI for more information. The model was trained on a Stability AI cluster with 8 nodes, each equipped with 8 A100 80GB GPUs, using internal scripts for SFT steps and HuggingFace's Alignment Handbook scripts for DPO training.",
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        "name": "Qwen 1.5",
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        "name": "Phi 2",
        "description": "Phi-2 is a 2.7 billion parameter Transformer model, an extension of Phi-1.5, with additional training data including synthetic NLP texts and curated web content. It demonstrates near state-of-the-art performance in benchmarks for common sense, language understanding, and logical reasoning within its parameter class. Phi-2 has not undergone reinforcement learning fine-tuning and is open-source, aimed at enabling safety research like toxicity reduction and bias understanding. It is designed for QA, chat, and code formats and has a context length of 2048 tokens. The model was trained on 250 billion tokens from a dataset combining AOAI GPT-3.5 synthetic data and filtered web data, using 1.4 trillion training tokens. It utilized 96xA100-80G GPUs over a span of 14 days. Phi-2 is released under the MIT license.",
        "author": {
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        "datePublished": "2023-08-27T18:17:14.000Z",
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