Text Generation
Transformers
English
Spanish
national-parks
state-parks
tourism
travel-assistant
outdoor-recreation
live-learning
real-time-data
Eval Results (legacy)
Instructions to use ajc2195/LLMTravelStateNationalPark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajc2195/LLMTravelStateNationalPark with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ajc2195/LLMTravelStateNationalPark")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajc2195/LLMTravelStateNationalPark", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ajc2195/LLMTravelStateNationalPark with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ajc2195/LLMTravelStateNationalPark" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ajc2195/LLMTravelStateNationalPark", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ajc2195/LLMTravelStateNationalPark
- SGLang
How to use ajc2195/LLMTravelStateNationalPark 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 "ajc2195/LLMTravelStateNationalPark" \ --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": "ajc2195/LLMTravelStateNationalPark", "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 "ajc2195/LLMTravelStateNationalPark" \ --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": "ajc2195/LLMTravelStateNationalPark", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ajc2195/LLMTravelStateNationalPark with Docker Model Runner:
docker model run hf.co/ajc2195/LLMTravelStateNationalPark
README.md exists but content is empty.
Model tree for ajc2195/LLMTravelStateNationalPark
Base model
meta-llama/Llama-2-7b-chat-hfEvaluation results
- Information Accuracy on National Parks Knowledge Datasetself-reported94.700
- Response Quality (BLEU) on National Parks Knowledge Datasetself-reported0.850
- Content Relevance (ROUGE-L) on National Parks Knowledge Datasetself-reported0.820
- Average Response Time (ms) on National Parks Knowledge Datasetself-reported180.000
- Park Coverage Percentage on National Parks Knowledge Datasetself-reported98.200