Text Generation
Transformers
PyTorch
English
qheart
feature-extraction
medical
ecg
question-answering
multimodal
custom_code
Instructions to use Manhph2211/Q-HEART with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manhph2211/Q-HEART with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Manhph2211/Q-HEART", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Manhph2211/Q-HEART", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Manhph2211/Q-HEART with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Manhph2211/Q-HEART" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Manhph2211/Q-HEART", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Manhph2211/Q-HEART
- SGLang
How to use Manhph2211/Q-HEART 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 "Manhph2211/Q-HEART" \ --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": "Manhph2211/Q-HEART", "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 "Manhph2211/Q-HEART" \ --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": "Manhph2211/Q-HEART", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Manhph2211/Q-HEART with Docker Model Runner:
docker model run hf.co/Manhph2211/Q-HEART
| { | |
| "model_type": "qheart", | |
| "architectures": ["QHEARTForECGQA"], | |
| "auto_map": { | |
| "AutoConfig": "configuration_qheart.QHEARTConfig", | |
| "AutoModel": "modeling_qheart.QHEARTForECGQA", | |
| "AutoModelForCausalLM": "modeling_qheart.QHEARTForECGQA" | |
| }, | |
| "encoder_layers": 12, | |
| "encoder_embed_dim": 768, | |
| "encoder_ffn_embed_dim": 3072, | |
| "encoder_attention_heads": 12, | |
| "layer_norm_first": false, | |
| "dropout": 0.1, | |
| "attention_dropout": 0.1, | |
| "activation_dropout": 0.0, | |
| "encoder_layerdrop": 0.0, | |
| "dropout_input": 0.1, | |
| "dropout_features": 0.1, | |
| "apply_mask": false, | |
| "mask_length": 10, | |
| "mask_prob": 0.0, | |
| "mask_selection": "static", | |
| "mask_other": 0.0, | |
| "no_mask_overlap": false, | |
| "mask_min_space": 1, | |
| "mask_channel_length": 10, | |
| "mask_channel_prob": 0.0, | |
| "mask_channel_selection": "static", | |
| "mask_channel_other": 0.0, | |
| "no_mask_channel_overlap": false, | |
| "mask_channel_min_space": 1, | |
| "extractor_mode": "default", | |
| "conv_feature_layers": "[(256, 2, 2)] * 4", | |
| "in_d": 12, | |
| "conv_bias": false, | |
| "feature_grad_mult": 1.0, | |
| "conv_pos": 128, | |
| "conv_pos_groups": 16, | |
| "load_pretrained_weights": false, | |
| "pretrained_model_path": "", | |
| "vocab_size": 32128, | |
| "hidden_dim": 768, | |
| "num_layers": 6, | |
| "num_heads": 12, | |
| "drop_rate": 0.1, | |
| "num_top_layer": 6, | |
| "mim_layer": 3, | |
| "mim_prob": 0.75, | |
| "mim_decoder_hidden_dim": 384, | |
| "mim_decoder_num_layers": 4, | |
| "mim_decoder_num_heads": 6, | |
| "max_text_size": 256, | |
| "llm_model_type": "meta-llama/Llama-3.2-1B-Instruct", | |
| "mapping_type": "Transformer", | |
| "prefix_length": 12, | |
| "clip_length": 12 | |
| } | |