Instructions to use alexandrosthegreat/help with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexandrosthegreat/help with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="alexandrosthegreat/help")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("alexandrosthegreat/help") model = AutoModelForImageTextToText.from_pretrained("alexandrosthegreat/help") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use alexandrosthegreat/help with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alexandrosthegreat/help" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alexandrosthegreat/help", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/alexandrosthegreat/help
- SGLang
How to use alexandrosthegreat/help 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 "alexandrosthegreat/help" \ --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": "alexandrosthegreat/help", "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 "alexandrosthegreat/help" \ --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": "alexandrosthegreat/help", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use alexandrosthegreat/help with Docker Model Runner:
docker model run hf.co/alexandrosthegreat/help
Commit ·
e2ea10e
1
Parent(s): fd0d2f9
Upload 8 files
Browse files- config.json +26 -0
- generation_config.json +7 -0
- preprocessor_config.json +24 -0
- special_tokens_map.json +7 -0
- tf_model.h5 +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +21 -0
- vocab.txt +0 -0
config.json
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{
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"_name_or_path": "Salesforce/blip-image-captioning-base",
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"architectures": [
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"BlipForConditionalGeneration"
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],
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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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"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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"initializer_factor": 1.0,
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"model_type": "blip_text_model",
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"num_attention_heads": 12
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},
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"torch_dtype": "float32",
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"transformers_version": "4.33.1",
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"vision_config": {
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"dropout": 0.0,
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"initializer_factor": 1.0,
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"initializer_range": 0.02,
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"model_type": "blip_vision_model",
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"num_channels": 3
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}
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}
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generation_config.json
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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": "4.33.1"
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}
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preprocessor_config.json
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{
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"do_convert_rgb": true,
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"do_normalize": true,
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"do_rescale": true,
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"do_resize": true,
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"image_mean": [
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0.48145466,
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0.4578275,
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0.40821073
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],
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"image_processor_type": "BlipImageProcessor",
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"image_std": [
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0.26862954,
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0.26130258,
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0.27577711
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],
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"processor_class": "BlipProcessor",
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"height": 384,
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"width": 384
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}
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}
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tf_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:6f6fbe057af12fd76278030eaae9a6db421e9bd8aa45ee23241a8097ae1b18a6
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size 990275048
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tokenizer.json
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tokenizer_config.json
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{
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"mask_token": "[MASK]",
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 512,
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"never_split": null,
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"pad_token": "[PAD]",
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"processor_class": "BlipProcessor",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"trust_remote_code": false,
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"unk_token": "[UNK]"
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}
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vocab.txt
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