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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분류 준비 중

입력 미확인→ 출력 미확인

요금 미확인멀티모달 및 채널 요금 별도 확인

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비교 추가출처 / 문서 ↗

Solidity-LLM

Chain-GPT

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

기본 정보 등록 · 상세 정보 보완 예정

텍스트 생성

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

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비교 추가출처 / 문서 ↗

Theia-Llama-3.1-8B-v1.1-GGUF

Chainbase-Labs

**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.

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텍스트 생성

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비교 추가출처 / 문서 ↗

Theia-Llama-3.1-8B-v1-GGUF

Chainbase-Labs

**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.

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텍스트 생성

입력 텍스트→ 출력 텍스트

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비교 추가출처 / 문서 ↗