Instructions to use basaadi/tifinagh-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use basaadi/tifinagh-ocr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="basaadi/tifinagh-ocr")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("basaadi/tifinagh-ocr") model = AutoModelForImageTextToText.from_pretrained("basaadi/tifinagh-ocr") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use basaadi/tifinagh-ocr with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "basaadi/tifinagh-ocr" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "basaadi/tifinagh-ocr", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/basaadi/tifinagh-ocr
- SGLang
How to use basaadi/tifinagh-ocr 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 "basaadi/tifinagh-ocr" \ --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": "basaadi/tifinagh-ocr", "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 "basaadi/tifinagh-ocr" \ --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": "basaadi/tifinagh-ocr", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use basaadi/tifinagh-ocr with Docker Model Runner:
docker model run hf.co/basaadi/tifinagh-ocr
File size: 2,358 Bytes
86117a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | {
"architectures": [
"VisionEncoderDecoderModel"
],
"decoder": {
"_name_or_path": "",
"activation_dropout": 0.0,
"activation_function": "gelu",
"add_cross_attention": true,
"architectures": null,
"attention_dropout": 0.0,
"bos_token_id": 0,
"chunk_size_feed_forward": 0,
"cross_attention_hidden_size": null,
"d_model": 384,
"decoder_attention_heads": 6,
"decoder_ffn_dim": 1536,
"decoder_layerdrop": 0.0,
"decoder_layers": 3,
"decoder_start_token_id": 2,
"dropout": 0.1,
"dtype": "float32",
"eos_token_id": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"init_std": 0.02,
"is_decoder": true,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layernorm_embedding": true,
"max_position_embeddings": 128,
"model_type": "trocr",
"output_attentions": false,
"output_hidden_states": false,
"pad_token_id": 1,
"problem_type": null,
"return_dict": true,
"scale_embedding": false,
"tie_word_embeddings": true,
"use_cache": true,
"use_learned_position_embeddings": true,
"vocab_size": 81
},
"decoder_start_token_id": 1,
"dtype": "float32",
"encoder": {
"_name_or_path": "",
"architectures": null,
"attention_probs_dropout_prob": 0.0,
"chunk_size_feed_forward": 0,
"dtype": "float32",
"encoder_stride": 16,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.0,
"hidden_size": 384,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"image_size": [
64,
256
],
"initializer_range": 0.02,
"intermediate_size": 1536,
"is_encoder_decoder": false,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 6,
"num_channels": 3,
"num_hidden_layers": 6,
"output_attentions": false,
"output_hidden_states": false,
"patch_size": 16,
"pooler_act": "tanh",
"pooler_output_size": 384,
"problem_type": null,
"qkv_bias": true,
"return_dict": true
},
"eos_token_id": 2,
"is_encoder_decoder": true,
"model_type": "vision-encoder-decoder",
"pad_token_id": 0,
"tie_word_embeddings": false,
"transformers_version": "5.5.4",
"vocab_size": 81
}
|