Instructions to use RobCzikkel/DoctorGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobCzikkel/DoctorGPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RobCzikkel/DoctorGPT")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RobCzikkel/DoctorGPT") model = AutoModelForCausalLM.from_pretrained("RobCzikkel/DoctorGPT") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RobCzikkel/DoctorGPT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RobCzikkel/DoctorGPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RobCzikkel/DoctorGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RobCzikkel/DoctorGPT
- SGLang
How to use RobCzikkel/DoctorGPT 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 "RobCzikkel/DoctorGPT" \ --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": "RobCzikkel/DoctorGPT", "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 "RobCzikkel/DoctorGPT" \ --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": "RobCzikkel/DoctorGPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RobCzikkel/DoctorGPT with Docker Model Runner:
docker model run hf.co/RobCzikkel/DoctorGPT
metadata
license: mit
base_model: microsoft/BioGPT-Large
tags:
- generated_from_trainer
model-index:
- name: bioGPT
results: []
bioGPT
This model is a fine-tuned version of microsoft/BioGPT-Large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1114
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.25 | 51 | 1.2418 |
| 1.3267 | 0.5 | 102 | 1.1900 |
| 1.3267 | 0.75 | 153 | 1.1348 |
| 1.1237 | 0.99 | 204 | 1.0887 |
| 1.1237 | 1.24 | 255 | 1.1018 |
| 0.7527 | 1.49 | 306 | 1.0770 |
| 0.7527 | 1.74 | 357 | 1.0464 |
| 0.7281 | 1.99 | 408 | 1.0233 |
| 0.7281 | 2.24 | 459 | 1.1212 |
| 0.4262 | 2.49 | 510 | 1.1177 |
| 0.4262 | 2.73 | 561 | 1.1125 |
| 0.4124 | 2.98 | 612 | 1.1114 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0