Instructions to use Lifan-Z/protGPT2_2025_LoRA_r4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Lifan-Z/protGPT2_2025_LoRA_r4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("nferruz/ProtGPT2") model = PeftModel.from_pretrained(base_model, "Lifan-Z/protGPT2_2025_LoRA_r4") - Transformers
How to use Lifan-Z/protGPT2_2025_LoRA_r4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Lifan-Z/protGPT2_2025_LoRA_r4")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lifan-Z/protGPT2_2025_LoRA_r4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Lifan-Z/protGPT2_2025_LoRA_r4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Lifan-Z/protGPT2_2025_LoRA_r4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lifan-Z/protGPT2_2025_LoRA_r4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Lifan-Z/protGPT2_2025_LoRA_r4
- SGLang
How to use Lifan-Z/protGPT2_2025_LoRA_r4 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 "Lifan-Z/protGPT2_2025_LoRA_r4" \ --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": "Lifan-Z/protGPT2_2025_LoRA_r4", "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 "Lifan-Z/protGPT2_2025_LoRA_r4" \ --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": "Lifan-Z/protGPT2_2025_LoRA_r4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Lifan-Z/protGPT2_2025_LoRA_r4 with Docker Model Runner:
docker model run hf.co/Lifan-Z/protGPT2_2025_LoRA_r4
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Download README.md from Lifan-Z/protGPT2_2025_LoRA_r4: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/Lifan-Z/protGPT2_2025_LoRA_r4/resolve/main/README.md
- Command line
-
hf download hf://Lifan-Z/protGPT2_2025_LoRA_r4/README.md
-
curl -L -o README.md https://huggingface.co/Lifan-Z/protGPT2_2025_LoRA_r4/resolve/main/README.md
1.39 kB
| library_name: peft | |
| license: apache-2.0 | |
| base_model: nferruz/ProtGPT2 | |
| tags: | |
| - base_model:adapter:nferruz/ProtGPT2 | |
| - lora | |
| - transformers | |
| pipeline_tag: text-generation | |
| model-index: | |
| - name: protGPT2_2025_LoRA_r4 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # protGPT2_2025_LoRA_r4 | |
| This model is a fine-tuned version of [nferruz/ProtGPT2](https://huggingface.co/nferruz/ProtGPT2) on an unknown dataset. | |
| ## 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: 0.0001 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 64 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 4 | |
| - training_steps: 1000 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.17.1 | |
| - Transformers 4.57.1 | |
| - Pytorch 2.8.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.1 |