Liuhaotian
Rank:Average Model Cost: $0.0000
Number of Runs: 3,295
Models by this creator
LLaVA-Lightning-MPT-7B-preview
LLaVA-Lightning-MPT-7B-preview
Platform did not provide a description for this model.
$-/run
1.8K
Huggingface
LLaVA-13b-delta-v0
LLaVA-13b-delta-v0
NOTE: This "delta model" cannot be used directly.Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights.See https://github.com/haotian-liu/LLaVA#llava-weights for instructions. LLaVA Model Card Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Model date: LLaVA was trained in April 2023. Paper or resources for more information: https://llava-vl.github.io/ License: Apache License 2.0 Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues Intended use Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. Training dataset 595K filtered image-text pairs from CC3M. 150K GPT-generated multimodal instruction-following data. Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs. We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset. See https://llava-vl.github.io/ for more details.
$-/run
505
Huggingface
LLaVA-Lightning-7B-delta-v1-1
LLaVA-Lightning-7B-delta-v1-1
NOTE: This "delta model" cannot be used directly.Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights.See https://github.com/haotian-liu/LLaVA#llava-weights for instructions. LLaVA Model Card Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Model date: LLaVA-Lightning was trained in May 2023. Paper or resources for more information: https://llava-vl.github.io/ License: Apache License 2.0 Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues Intended use Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. Training dataset 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. 80K GPT-generated multimodal instruction-following data. Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs. We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset. See https://llava-vl.github.io/ for more details.
$-/run
129
Huggingface
LLaVA-13b-delta-v1-1
$-/run
85
Huggingface
LLaVA-13b-delta-v0-science_qa
LLaVA-13b-delta-v0-science_qa
NOTE: This "delta model" cannot be used directly.Users have to apply it on top of the original LLaMA weights to get actual LLaVA weights.See https://github.com/haotian-liu/LLaVA#llava-weights for instructions. LLaVA Model Card Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. This model is finetuned on ScienceQA dataset. Model date: LLaVA was trained in April 2023. Paper or resources for more information: https://llava-vl.github.io/ License: Apache License 2.0 Where to send questions or comments about the model: https://github.com/haotian-liu/LLaVA/issues Intended use Primary intended uses: The primary use of LLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. Training dataset 595K filtered image-text pairs from CC3M. ScienceQA dataset. Evaluation dataset A preliminary evaluation of the model quality is conducted by creating a set of 90 visual reasoning questions from 30 unique images randomly sampled from COCO val 2014 and each is associated with three types of questions: conversational, detailed description, and complex reasoning. We utilize GPT-4 to judge the model outputs. We also evaluate our model on the ScienceQA dataset. Our synergy with GPT-4 sets a new state-of-the-art on the dataset. See https://llava-vl.github.io/ for more details.
$-/run
57
Huggingface
llava-vicuna-7b-v1.1-lcs_558k-instruct_80k_1e-lora-preview_alpha
llava-vicuna-7b-v1.1-lcs_558k-instruct_80k_1e-lora-preview_alpha
Platform did not provide a description for this model.
$-/run
32
Huggingface
LLaVA-Pretrained-Projectors
LLaVA-Pretrained-Projectors
Platform did not provide a description for this model.
$-/run
0
Huggingface