Image-Text-to-Text
Transformers
TensorBoard
Safetensors
blip
Generated from Trainer
Eval Results (legacy)
Instructions to use 0x-Jayveersinh-Raj/fabric_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 0x-Jayveersinh-Raj/fabric_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="0x-Jayveersinh-Raj/fabric_classifier")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("0x-Jayveersinh-Raj/fabric_classifier") model = AutoModelForImageTextToText.from_pretrained("0x-Jayveersinh-Raj/fabric_classifier") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 0x-Jayveersinh-Raj/fabric_classifier with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "0x-Jayveersinh-Raj/fabric_classifier" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0x-Jayveersinh-Raj/fabric_classifier", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/0x-Jayveersinh-Raj/fabric_classifier
- SGLang
How to use 0x-Jayveersinh-Raj/fabric_classifier with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "0x-Jayveersinh-Raj/fabric_classifier" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0x-Jayveersinh-Raj/fabric_classifier", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "0x-Jayveersinh-Raj/fabric_classifier" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0x-Jayveersinh-Raj/fabric_classifier", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 0x-Jayveersinh-Raj/fabric_classifier with Docker Model Runner:
docker model run hf.co/0x-Jayveersinh-Raj/fabric_classifier
Upload BlipForConditionalGeneration
Browse files- README.md +3 -3
- config.json +45 -42
- generation_config.json +7 -0
- model.safetensors +2 -2
README.md
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- name: fabric_classifier
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: arrow
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type: arrow
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split: train
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args: default
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metrics:
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type: accuracy
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value: 0.5
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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- name: fabric_classifier
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results:
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- task:
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type: image-classification
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name: Image Classification
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dataset:
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name: arrow
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type: arrow
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split: train
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args: default
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metrics:
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value: 0.5
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name: Accuracy
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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config.json
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"id2label": {
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"0": "Cotton",
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"1": "Silk",
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"8": "Cotton Silk",
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"9": "Linen"
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},
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"image_size": 224,
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"initializer_range": 0.02,
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"dtype": "float32",
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"image_text_hidden_size": 256,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"label_smoothing": 0.0,
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"logit_scale_init_value": 2.6592,
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"model_type": "blip",
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"projection_dim": 512,
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"text_config": {
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"attention_probs_dropout_prob": 0.0,
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"dtype": "float32",
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"encoder_hidden_size": 768,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.0,
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"hidden_size": 768,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label_smoothing": 0.0,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "blip_text_model",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"projection_dim": 768,
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"use_cache": true,
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"vocab_size": 30524
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},
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"transformers_version": "4.57.0",
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"vision_config": {
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"attention_dropout": 0.0,
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"dropout": 0.0,
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"dtype": "float32",
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"hidden_act": "gelu",
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"hidden_size": 768,
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"image_size": 384,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-05,
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"model_type": "blip_vision_model",
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"num_attention_heads": 12,
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"num_channels": 3,
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"num_hidden_layers": 12,
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"patch_size": 16,
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"projection_dim": 512
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}
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{
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"_from_model_config": true,
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"bos_token_id": 30522,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.57.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:026963a8993b3ad94f559582db605a5fbc7a8f647ee113d9428ec4d85abe609c
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size 895947208
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