Instructions to use Samoed/llama-2-7b-airplane-tickets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Samoed/llama-2-7b-airplane-tickets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Samoed/llama-2-7b-airplane-tickets")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Samoed/llama-2-7b-airplane-tickets") model = AutoModelForCausalLM.from_pretrained("Samoed/llama-2-7b-airplane-tickets", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Samoed/llama-2-7b-airplane-tickets with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Samoed/llama-2-7b-airplane-tickets" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Samoed/llama-2-7b-airplane-tickets", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Samoed/llama-2-7b-airplane-tickets
- SGLang
How to use Samoed/llama-2-7b-airplane-tickets 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 "Samoed/llama-2-7b-airplane-tickets" \ --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": "Samoed/llama-2-7b-airplane-tickets", "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 "Samoed/llama-2-7b-airplane-tickets" \ --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": "Samoed/llama-2-7b-airplane-tickets", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Samoed/llama-2-7b-airplane-tickets with Docker Model Runner:
docker model run hf.co/Samoed/llama-2-7b-airplane-tickets
Download pytorch_model-00002-of-00002.bin from Samoed/llama-2-7b-airplane-tickets: direct link, hf CLI and curl.
- Browser
- Download file 3.5 GB
-
https://huggingface.co/Samoed/llama-2-7b-airplane-tickets/resolve/main/pytorch_model-00002-of-00002.bin
- Command line
-
hf download hf://Samoed/llama-2-7b-airplane-tickets/pytorch_model-00002-of-00002.bin
-
curl -L -o pytorch_model-00002-of-00002.bin https://huggingface.co/Samoed/llama-2-7b-airplane-tickets/resolve/main/pytorch_model-00002-of-00002.bin
3.5 GB
- Xet hash:
- f9fadf32609236c2f022a03d97fa816ad837df163e7d8c8acb37fac853d6949e
- Size of remote file:
- 3.5 GB
- SHA256:
- b440f16b030c28e8340a2c4ce11bddc333b7bed9f0e7668632bf2b50ac208fb3
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