To Use LLaVA-1.6 checkpoints, your llava package version must be newer than 1.2.0. Instructions on how to upgrade.
If you are interested in including any other details in Model Zoo, please open an issue :)
The model weights below are merged weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license.
| Version | LLM | Schedule | Checkpoint | MMMU | MathVista | VQAv2 | GQA | VizWiz | SQA | TextVQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED-IMG | LLaVA-Bench-Wild | MM-Vet |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LLaVA-1.6 | Vicuna-7B | full_ft-1e | liuhaotian/llava-v1.6-vicuna-7b | 35.8 | 34.6 | 81.8 | 64.2 | 57.6 | 70.1 | 64.9 | 86.5 | 1519/332 | 67.4 | 60.6 | 70.2 | 81.6 | 43.9 |
| LLaVA-1.6 | Vicuna-13B | full_ft-1e | liuhaotian/llava-v1.6-vicuna-13b | 36.2 | 35.3 | 82.8 | 65.4 | 60.5 | 73.6 | 67.1 | 86.2 | 1575/326 | 70 | 64.4 | 71.9 | 87.3 | 48.4 |
| LLaVA-1.6 | Mistral-7B | full_ft-1e | liuhaotian/llava-v1.6-mistral-7b | 35.3 | 37.7 | 82.2 | 64.8 | 60.0 | 72.8 | 65.7 | 86.7 | 1498/321 | 68.7 | 61.2 | 72.2 | 83.2 | 47.3 |
| LLaVA-1.6 | Hermes-Yi-34B | full_ft-1e | liuhaotian/llava-v1.6-34b | 51.1 | 46.5 | 83.7 | 67.1 | 63.8 | 81.8 | 69.5 | 87.7 | 1631/397 | 79.3 | 79 | 75.9 | 89.6 | 57.4 |
LLaVA-1.6-34B outperforms Gemini Pro on benchmarks like MMMU and MathVista.
| Version | Size | Schedule | Checkpoint | VQAv2 | GQA | VizWiz | SQA | TextVQA | POPE | MME | MM-Bench | MM-Bench-CN | SEED | LLaVA-Bench-Wild | MM-Vet |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LLaVA-1.5 | 7B | full_ft-1e | liuhaotian/llava-v1.5-7b | 78.5 | 62.0 | 50.0 | 66.8 | 58.2 | 85.9 | 1510.7 | 64.3 | 58.3 | 58.6 | 65.4 | 31.1 |
| LLaVA-1.5 | 13B | full_ft-1e | liuhaotian/llava-v1.5-13b | 80.0 | 63.3 | 53.6 | 71.6 | 61.3 | 85.9 | 1531.3 | 67.7 | 63.6 | 61.6 | 72.5 | 36.1 |
| LLaVA-1.5 | 7B | lora-1e | liuhaotian/llava-v1.5-7b-lora | 79.1 | 63.0 | 47.8 | 68.4 | 58.2 | 86.4 | 1476.9 | 66.1 | 58.9 | 60.1 | 67.9 | 30.2 |
| LLaVA-1.5 | 13B | lora-1e | liuhaotian/llava-v1.5-13b-lora | 80.0 | 63.3 | 58.9 | 71.2 | 60.2 | 86.7 | 1541.7 | 68.5 | 61.5 | 61.3 | 69.5 | 38.3 |
Base model: Vicuna v1.5. Training logs: wandb.
LLaVA-1.5 achieves SoTA performance across 11 benchmarks.
Note: We recommend using the most capable LLaVA-v1.6 series above for the best performance.
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | LLaVA-Bench-Conv | LLaVA-Bench-Detail | LLaVA-Bench-Complex | LLaVA-Bench-Overall | Download |
|---|---|---|---|---|---|---|---|---|---|---|
| Vicuna-13B-v1.3 | CLIP-L-336px | LCS-558K | 1e | LLaVA-Instruct-80K | proj-1e, lora-1e | 64.3 | 55.9 | 81.7 | 70.1 | LoRA LoRA-Merged |
| LLaMA-2-13B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | 56.7 | 58.6 | 80.0 | 67.9 | ckpt |
| LLaMA-2-7B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | lora-1e | 51.2 | 58.9 | 71.6 | 62.8 | LoRA |
These are projector weights we have pretrained. You can use these projector weights for visual instruction tuning. They are just pretrained on image-text pairs and are NOT instruction-tuned, which means they do NOT follow instructions as well as our official models and can output repetitive, lengthy, and garbled outputs. If you want to have nice conversations with LLaVA, use the checkpoints above (LLaVA v1.6).
NOTE: These projector weights are only compatible with llava>=1.0.0. Please check out the latest codebase if your local code version is below v1.0.0.
NOTE: When you use our pretrained projector for visual instruction tuning, it is very important to use the same base LLM and vision encoder as the one we used for pretraining the projector. Otherwise, the performance will be very poor.
When using these projector weights to instruction-tune your LMM, please make sure that these options are correctly set as follows,
--mm_use_im_start_end False
--mm_use_im_patch_token False| Base LLM | Vision Encoder | Projection | Pretrain Data | Pretraining schedule | Download |
|---|---|---|---|---|---|
| Vicuna-13B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | projector |
| Vicuna-7B-v1.5 | CLIP-L-336px | MLP-2x | LCS-558K | 1e | projector |
| LLaMA-2-13B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | projector |
| LLaMA-2-7B-Chat | CLIP-L-336px | Linear | LCS-558K | 1e | projector |
| LLaMA-2-13B-Chat | CLIP-L | Linear | LCS-558K | 1e | projector |
| LLaMA-2-7B-Chat | CLIP-L | Linear | LCS-558K | 1e | projector |
| Vicuna-13B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | projector |
| Vicuna-7B-v1.3 | CLIP-L-336px | Linear | LCS-558K | 1e | projector |
| Vicuna-13B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | projector |
| Vicuna-7B-v1.3 | CLIP-L | Linear | LCS-558K | 1e | projector |
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
|---|---|---|---|---|---|---|
| Vicuna-13B-v1.3 | CLIP-L | LCS-558K | 1e | ScienceQA | full_ft-12e | ckpt |
The model weights below are merged weights. You do not need to apply delta. The usage of LLaVA checkpoints should comply with the base LLM's model license.
| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
|---|---|---|---|---|---|---|
| MPT-7B-Chat | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | preview |
The model weights below are delta weights. The usage of LLaVA checkpoints should comply with the base LLM's model license: LLaMA.
You can add our delta to the original LLaMA weights to obtain the LLaVA weights.
Instructions:
- Get the original LLaMA weights in the huggingface format by following the instructions here.
- Use the following scripts to get LLaVA weights by applying our delta. It will automatically download delta weights from our Hugging Face account. In the script below, we use the delta weights of
liuhaotian/LLaVA-7b-delta-v0as an example. It can be adapted for other delta weights by changing the--deltaargument (and base/target accordingly).
python3 -m llava.model.apply_delta \
--base /path/to/llama-7b \
--target /output/path/to/LLaVA-7B-v0 \
--delta liuhaotian/LLaVA-7b-delta-v0| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Finetuning Data | Finetuning schedule | Download |
|---|---|---|---|---|---|---|
| Vicuna-13B-v1.1 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | delta-weights |
| Vicuna-7B-v1.1 | CLIP-L | LCS-558K | 1e | LLaVA-Instruct-80K | full_ft-1e | delta-weights |
| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | delta-weights |
| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | ScienceQA | full_ft-12e | delta-weights |
| Vicuna-7B-v0 | CLIP-L | CC-595K | 1e | LLaVA-Instruct-158K | full_ft-3e | delta-weights |
The following projector weights are deprecated, and the support for them may be removed in the future. They do not support zero-shot inference. Please use the projector weights in the table above if possible.
NOTE: When you use our pretrained projector for visual instruction tuning, it is very important to use the same base LLM and vision encoder as the one we used for pretraining the projector. Otherwise, the performance will be very bad.
When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows,
--mm_use_im_start_end True
--mm_use_im_patch_token False| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download |
|---|---|---|---|---|
| Vicuna-7B-v1.1 | CLIP-L | LCS-558K | 1e | projector |
| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | projector |
| Vicuna-7B-v0 | CLIP-L | CC-595K | 1e | projector |
When using these projector weights to instruction tune your LMM, please make sure that these options are correctly set as follows,
--mm_use_im_start_end False
--mm_use_im_patch_token False| Base LLM | Vision Encoder | Pretrain Data | Pretraining schedule | Download |
|---|---|---|---|---|
| Vicuna-13B-v0 | CLIP-L | CC-595K | 1e | projector |