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Merge pull request #48 from lmstudio-ai/catalog-update-dec23
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| "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.", | ||
| "author": { | ||
| "name": "Mistral AI", | ||
| "url": "https://mistral.ai/", | ||
| "blurb": "Mistral AI's mission is to spearhead the revolution of open models." | ||
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| "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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| "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": "2023-10-29T11:30:13", | ||
| "name": "Deepseek Coder", | ||
| "description": "Deepseek Coder is a collection of code language models with sizes ranging from 1B to 33B parameters, trained on a dataset comprising 2 trillion tokens (87% code, 13% natural language in English and Chinese). It is designed for project-level code completion and infilling, utilizing a 16K token window size and an additional fill-in-the-blank task. The models demonstrate leading performance on several programming benchmarks. The 6.7B parameter variant, deepseek-coder-6.7b-instruct, is fine-tuned on 2 billion tokens of instructional data. The code repository is MIT licensed, and the models support commercial use under the Model License.", | ||
| "author": { | ||
| "name": "DeepSeek", | ||
| "url": "https://huggingface.co/deepseek-ai", | ||
| "blurb": "DeepSeek (深度求索), founded in 2023, is a Chinese company dedicated to making AGI a reality" | ||
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| { | ||
| "_descriptorVersion": "0.0.1", | ||
| "datePublished": "2023-11-21T16:28:30", | ||
| "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.", | ||
| "author": { | ||
| "name": "Stability AI", | ||
| "url": "https://stability.ai/", | ||
| "blurb": "Stability AI is developing cutting-edge open AI models for Image, Language, Audio, Video, 3D and Biology." | ||
| }, | ||
| "numParameters": "3B", | ||
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