stablecode-completion-alpha-3b-4k
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stablecode-instruct-alpha-3b
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View details ↗stablelm-2-1_6b
Stability AI`Stable LM 2 1.6B` is a 1.6 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs. The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications: The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the HuggingFace Hub: Falcon RefinedWeb extract (Penedo et al., 2023), RedPajama-Data (Together Computer., 2023) and The Pile (Gao et al., 2020) both without the *Books3* subset, and StarCoder (Li et al., 2023). We further supplement our training with multi-lingual data from CulturaX (Nguyen et al., 2023) and, in particular, from its OSCAR corpora, as well as restructured data in the style of Yuan & Liu (2022).
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View details ↗stablelm-2-1_6b-chat
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View details ↗stablelm-2-12b
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View details ↗stablelm-2-12b-chat
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View details ↗stablelm-2-12b-chat-GGUF
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View details ↗stablelm-2-zephyr-1_6b
Stability AI`Stable LM 2 Zephyr 1.6B` is a 1.6 billion parameter instruction tuned language model inspired by HugginFaceH4's Zephyr 7B training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing Direct Preference Optimization (DPO). The dataset is comprised of a mixture of open datasets large-scale datasets available on the HuggingFace Hub: 1. SFT Datasets - HuggingFaceH4/ultrachat_200k - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Open-Orca/SlimOrca - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - hkust-nlp/deita-10k-v0 The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about safety and limitations below.
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View details ↗stablelm-3b-4e1t
Stability AI`StableLM-3B-4E1T` is a 3 billion parameter decoder-only language model pre-trained on 1 trillion tokens of diverse English and code datasets for 4 epochs. The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications: The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the HuggingFace Hub: Falcon RefinedWeb extract (Penedo et al., 2023), RedPajama-Data (Together Computer., 2023) and The Pile (Gao et al., 2020) both without the *Books3* subset, and StarCoder (Li et al., 2023).
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View details ↗stablelm-base-alpha-3b
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View details ↗stablelm-base-alpha-3b-v2
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View details ↗stablelm-base-alpha-7b
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View details ↗stablelm-base-alpha-7b-v2
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View details ↗stablelm-tuned-alpha-3b
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View details ↗stablelm-tuned-alpha-7b
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View details ↗stablelm-zephyr-3b
Stability AI`StableLM Zephyr 3B` is a 3 billion parameter instruction tuned inspired by HugginFaceH4's Zephyr 7B training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using Direct Preference Optimization (DPO), evaluation for this model based on MT Bench and Alpaca Benchmark This format is also available through the tokenizer's `apply_chat_template` method: You can also see how to run a performance optimized version of this model here using OpenVINO from Intel.
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View details ↗sv3d
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View details ↗sv4d
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View details ↗sv4d2.0
Stability AI**Stable Video 4D 2.0 (SV4D 2.0)** is a generative model based on Stable Video 4D (SV4D), which takes in a single-view video of an object and generates multiple novel-view videos (4D image matrix) of that object. Please note: For individuals or organizations generating annual revenue of US $1,000,000 (or local currency equivalent) or more, regardless of the source of that revenue, you must obtain an enterprise commercial license directly from Stability AI before commercially using SV4D 2.0, or any derivative work of SV4D 2.0 or its outputs, such as “fine tune” or “low-rank adaption” models. You may submit a request for an Enterprise License at https://stability.ai/enterprise. Please refer to Stability AI’s Community License, available at https://stability.ai/license, for more information. We use renders from the Objaverse-XL dataset, available under the Open Data Commons Attribution License, utilizing our enhanced rendering method that more closely replicates the distribution of images found in the real world, significantly improving our model's ability to generalize. We filter objects based on the review of licenses and curated a subset suitable for our training needs.
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View details ↗tiny-random-stablelm-2
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View details ↗TripoSR
Stability AI> Try our new model: **SF3D** with several improvements such as faster generation and more game-ready assets. > > The model is available here and we also have a demo. We closely follow LRM network architecture for the model design, where TripoSR incorporates a series of technical advancements over the LRM model in terms of both data curation as well as model and training improvements. For more technical details and evaluations, please refer to our tech report. * **Developed by**: Stability AI, Tripo AI * **Model type**: Feed-forward 3D reconstruction from a single image * **License**: MIT * **Hardware**: We train `TripoSR` for 5 days on 22 GPU nodes each with 8 A100 40GB GPUs
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View details ↗sdols-2.0
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View details ↗sdols-2.0-fast
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View details ↗stable-diffusion
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