Text Generation
Transformers
PyTorch
English
llama
medical
text2text-generation
text-generation-inference
Instructions to use starmpcc/Asclepius-Llama2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use starmpcc/Asclepius-Llama2-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="starmpcc/Asclepius-Llama2-13B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("starmpcc/Asclepius-Llama2-13B") model = AutoModelForCausalLM.from_pretrained("starmpcc/Asclepius-Llama2-13B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use starmpcc/Asclepius-Llama2-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "starmpcc/Asclepius-Llama2-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "starmpcc/Asclepius-Llama2-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/starmpcc/Asclepius-Llama2-13B
- SGLang
How to use starmpcc/Asclepius-Llama2-13B 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 "starmpcc/Asclepius-Llama2-13B" \ --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": "starmpcc/Asclepius-Llama2-13B", "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 "starmpcc/Asclepius-Llama2-13B" \ --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": "starmpcc/Asclepius-Llama2-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use starmpcc/Asclepius-Llama2-13B with Docker Model Runner:
docker model run hf.co/starmpcc/Asclepius-Llama2-13B
Download tokenizer.model from starmpcc/Asclepius-Llama2-13B: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/starmpcc/Asclepius-Llama2-13B/resolve/main/tokenizer.model
- Command line
-
hf download hf://starmpcc/Asclepius-Llama2-13B/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/starmpcc/Asclepius-Llama2-13B/resolve/main/tokenizer.model
500 kB
- Xet hash:
- 91bf184ab12793d0754344f9095332759432e666320cc6c07f637af50e36db6f
- Size of remote file:
- 500 kB
- SHA256:
- 9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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