Text Generation
Transformers
Safetensors
PEFT
llama
protein
ptm
methylation
phosphorylation
ubiquitination
lora
text-generation-inference
Instructions to use jbenbudd/ptm-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jbenbudd/ptm-llama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jbenbudd/ptm-llama")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jbenbudd/ptm-llama") model = AutoModelForCausalLM.from_pretrained("jbenbudd/ptm-llama") - PEFT
How to use jbenbudd/ptm-llama with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jbenbudd/ptm-llama with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jbenbudd/ptm-llama" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jbenbudd/ptm-llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jbenbudd/ptm-llama
- SGLang
How to use jbenbudd/ptm-llama 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 "jbenbudd/ptm-llama" \ --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": "jbenbudd/ptm-llama", "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 "jbenbudd/ptm-llama" \ --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": "jbenbudd/ptm-llama", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jbenbudd/ptm-llama with Docker Model Runner:
docker model run hf.co/jbenbudd/ptm-llama

- Xet hash:
- 255f11ec2ba4da81d88df5b4368e8f41add082c3b214689abe8c864d898a7fb2
- Size of remote file:
- 176 kB
- SHA256:
- 32e96c1b65172c6a291ea52bd2ef3ac57d53fe868dc6fc2f856d3174d115f015
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.