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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- finetuned |
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- multimodal |
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- llava |
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base_model: mistralai/Mistral-7B-Instruct-v0.1 |
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dataset: sshh12/llava-finetune |
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inference: false |
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pipeline_tag: image-text-to-text |
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--- |
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These are weights for a version of `mistralai/Mistral-7B-Instruct-v0.1` finetuned for multimodal applications. |
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### Modalities |
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* CLIPVisionModality (use `<image>` in text and provide `images`, encoded as 10 tokens) |
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### Usage |
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GitHub: https://github.com/sshh12/multi_token (includes training scripts and basic inference server) |
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### Dataset |
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sshh12/llava-finetune (544610 examples) |
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``` |
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{'id': '000000033471', 'images': ['/data/llava_finetune_data/images/coco/train2017/train2017/000000033471.jpg'], 'messages': [{'content': '<image>\nWhat are the colors of the bus in the image?', 'role': 'user'}, {'content': 'The bus in the image is white and red.', 'role': 'assistant'}, {'content': 'What feature can be seen on the back of the bus?', 'role': 'user'}, {'content': 'The back of the bus features an advertisement.', 'role': 'assistant'}, {'content': 'Is the bus driving down the street or pulled off to the side?', 'role': 'user'}, {'content': 'The bus is driving down the street, which is crowded with people and other vehicles.', 'role': 'assistant'}]} |
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``` |
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### Training Device(s) |
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``` |
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name, pci.bus_id, vbios_version |
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NVIDIA RTX A6000, 00000000:02:00.0, 94.02.5C.00.02 |
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``` |
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### Model |
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``` |
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MistralLMMForCausalLM.model = |
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PeftModelForCausalLM( |
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(base_model): LoraModel( |
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(model): MistralLMMForCausalLM( |
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(model): MistralLMMModel( |
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(embed_tokens): Embedding(32000, 4096) |
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(layers): ModuleList( |
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(0-31): 32 x MistralDecoderLayer( |
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(self_attn): MistralAttention( |
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(q_proj): lora.Linear( |
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(base_layer): Linear(in_features=4096, out_features=4096, bias=False) |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=64, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=64, out_features=4096, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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(k_proj): lora.Linear( |
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(base_layer): Linear(in_features=4096, out_features=1024, bias=False) |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=64, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=64, out_features=1024, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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(v_proj): lora.Linear( |
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(base_layer): Linear(in_features=4096, out_features=1024, bias=False) |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=64, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=64, out_features=1024, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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(o_proj): lora.Linear( |
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(base_layer): Linear(in_features=4096, out_features=4096, bias=False) |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=64, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=64, out_features=4096, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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(rotary_emb): MistralRotaryEmbedding() |
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) |
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(mlp): MistralMLP( |
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(gate_proj): lora.Linear( |
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(base_layer): Linear(in_features=4096, out_features=14336, bias=False) |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=64, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=64, out_features=14336, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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(up_proj): lora.Linear( |
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(base_layer): Linear(in_features=4096, out_features=14336, bias=False) |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=64, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=64, out_features=14336, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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(down_proj): lora.Linear( |
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(base_layer): Linear(in_features=14336, out_features=4096, bias=False) |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=14336, out_features=64, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=64, out_features=4096, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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(act_fn): SiLUActivation() |
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) |
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(input_layernorm): MistralRMSNorm() |
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(post_attention_layernorm): MistralRMSNorm() |
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) |
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) |
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(norm): MistralRMSNorm() |
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(vision_clip_lmm_projector): _MLPVectorProjector( |
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(mlps): ModuleList( |
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(0-9): 10 x Sequential( |
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(0): Linear(in_features=1024, out_features=4096, bias=True) |
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(1): GELU(approximate='none') |
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(2): Linear(in_features=4096, out_features=4096, bias=True) |
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) |
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) |
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) |
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) |
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(lm_head): Linear(in_features=4096, out_features=32000, bias=False) |
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) |
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) |
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) |
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``` |
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### Framework versions |
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- PEFT 0.7.0 |
