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cowork-companion

chadstthomas

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LLaVA-3D-7B

ChaimZhu

The LLaVA-3D model is a 7B parameter models trained on LLaVA-3D-Instruct-1M, based on LLaVA-v1.5-7B. The model was trained on LLaVA-3D-Instruct-1M and has the ability to interact with the single image for 2D tasks and posed RBG-D images for 3D tasks.

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Solidity-LLM

Chain-GPT

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Text generation
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**Theia-Llama-3.1-8B is an open-source crypto LLM, trained with carefully-designed dataset from the crypto field.** The training dataset is curated from two primary sources to create a comprehensive representation of blockchain projects. The first source is data collected from **CoinMarketCap**, focusing on the top **2000 projects** ranked by market capitalization. This includes a wide range of project-specific documents such as whitepapers, official blog posts, and news articles. The second core component of the dataset comprises detailed research reports on these projects gathered from various credible sources on the internet, providing in-depth insights into project fundamentals, development progress, and market impact. After constructing the dataset, both manual and algorithmic filtering are applied to ensure data accuracy and eliminate redundancy. The Theia-Llama-3.1-8B is fine-tuned from the base model (Llama-3.1-8B), specifically tailored for the cryptocurrency domain. We employed LoRA (Low-Rank Adaptation) to fine-tune the model effectively, leveraging its ability to adapt large pre-trained models to specific tasks with a smaller computational footprint. Our training methodology is further enhanced through the use of LLaMA Factory, an open-source training framework. We integrate **DeepSpeed**, Microsoft's distributed training engine, to optimize resource utilization and training efficiency. Techniques such as ZeRO (Zero Redundancy Optimizer), offload, sparse attention, 1-bit Adam, and pipeline parallelism are employed to accelerate the training process and reduce memory consumption. A fine-tuned model is also built using the novel D-DoRA, a decentralized training scheme, by our Chainbase Labs. Since the LoRA version is much easier to deploy and play with for developers, we release the LoRA version first for the Crypto AI community.

Text generation
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**Theia-Llama-3.1-8B-v1 is an open-source crypto LLM, trained with carefully-designed dataset from the crypto field.** The training dataset is curated from two primary sources to create a comprehensive representation of blockchain projects. The first source is data collected from **CoinMarketCap**, focusing on the top **2000 projects** ranked by market capitalization. This includes a wide range of project-specific documents such as whitepapers, official blog posts, and news articles. The second core component of the dataset comprises detailed research reports on these projects gathered from various credible sources on the internet, providing in-depth insights into project fundamentals, development progress, and market impact. After constructing the dataset, both manual and algorithmic filtering are applied to ensure data accuracy and eliminate redundancy. The Theia-Llama-3.1-8B-v1 is fine-tuned from the base model (Llama-3.1-8B), specifically tailored for the cryptocurrency domain. We employed LoRA (Low-Rank Adaptation) to fine-tune the model effectively, leveraging its ability to adapt large pre-trained models to specific tasks with a smaller computational footprint. Our training methodology is further enhanced through the use of LLaMA Factory, an open-source training framework. We integrate **DeepSpeed**, Microsoft's distributed training engine, to optimize resource utilization and training efficiency. Techniques such as ZeRO (Zero Redundancy Optimizer), offload, sparse attention, 1-bit Adam, and pipeline parallelism are employed to accelerate the training process and reduce memory consumption. A fine-tuned model is also built using the novel D-DoRA, a decentralized training scheme, by our Chainbase Labs. Since the LoRA version is much easier to deploy and play with for developers, we release the LoRA version first for the Crypto AI community.

Text generation
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AMPLIFY_120M

chandar-lab

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AMPLIFY_350M

chandar-lab

AMPLIFY is an efficient, state-of-the-art protein language model pre-trained using masked language modeling on UniRef100, OAS, and SCOP (UR100P). AMPLIFY can generate residue and protein embeddings, suggest mutations, differentiate disordered proteins from non-protein sequences, and much more. AMPLIFY is available in two sizes, 120M and 350M parameters, with the `_base` models not extended beyond 512 residues (Stage 1). The model architecture and pre-training procedure are detailed below. For more details, please refer to the accompanying paper.

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NeoBERT

chandar-lab

NeoBERT is a **next-generation encoder** model for English text representation, pre-trained from scratch on the RefinedWeb dataset. NeoBERT integrates state-of-the-art advancements in architecture, modern data, and optimized pre-training methodologies. It is designed for seamless adoption: it serves as a plug-and-play replacement for existing base models, relies on an **optimal depth-to-width ratio**, and leverages an extended context length of **4,096 tokens**. Despite its compact 250M parameter footprint, it is the most efficient model of its kind and achieves **state-of-the-art results** on the massive MTEB benchmark, outperforming BERT large, RoBERTa large, NomicBERT, and ModernBERT under identical fine-tuning conditions. - Paper: paper - Repository: github. If you would like to use sequence packing (un-padding), you will need to also install flash-attention:

Embeddings & similarity
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