✅ totally no tuning ✅ less than 20% extra time ✅ support 512 frames -->
The main features of tingxueronghua/chartllama-code are: Foundation Models, Specialized Domain Models.
Open-source alternatives to tingxueronghua/chartllama-code include: rshaojimmy/jiutian — [CVPR 2024] LION: Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge. openlmlab/moss — MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for… lianjiatech/belle — BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction… abacinlp/investlm — This is the repo for a new financial domain large language model, InvestLM, tuned on LLaMA-65B[1], using a carefully… baaivision/emu — Emu Series: Generative Multimodal Models from BAAI. alembics/disco-diffusion — This project is a diffusion-based AI art generator and animation framework used to create digital images and motion…
MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for deploying large language models capable of multi-turn dialogue, general-purpose response generation, and following complex instructions. The system functions as a tool-augmented framework that extends model knowledge through external plugins and tool-call loops. This allows the model to execute tasks via search engines and calculators to augment responses with external data. The project covers model training through supervised conversational fine-tuning and optimizes deployment
CVPR 2024 LION: Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge
BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction tuning framework. It provides a pipeline for training, evaluating, and deploying models optimized for natural language understanding and dialogue tasks in the Chinese language. The project is distinguished by its integrated approach to model refinement, combining the curation of multi-million entry instruction datasets with a distributed training pipeline. This pipeline supports both full fine-tuning and low-rank adaptation to optimize conversational performance. The system
This is the repo for a new financial domain large language model, InvestLM, tuned on LLaMA-65B1, using a carefully curated instruction dataset related to financial investment. We provide guidance on how to use InvestLM for inference.