Instructions to use ajc2195/LLMTravelStateNationalPark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

LLMTravelStateNationalPark / README.md

ajc2195

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5f45827 verified about 1 year ago

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metadata

license: mit
datasets:
  - nps-official-data
  - weather-gov-historical
  - recreation-gov-campgrounds
  - alltrails-conditions
  - social-media-park-feeds
  - visitor-reviews-tripadvisor
  - ranger-reports-database
  - wildlife-tracking-data
  - emergency-incidents-database
  - accessibility-surveys
language:
  - en
  - es
metrics:
  - accuracy
  - bleu
  - rouge
  - perplexity
  - response_time
  - knowledge_coverage
  - source_reliability
base_model: meta-llama/Llama-2-7b-chat-hf
new_version: 1.2.0
library_name: transformers
pipeline_tag: text-generation
tags:
  - national-parks
  - state-parks
  - tourism
  - travel-assistant
  - outdoor-recreation
  - live-learning
  - real-time-data
model-index:
  - name: national-park-llm
    results:
      - task:
          type: text-generation
          name: Park Information Generation
        dataset:
          type: custom
          name: National Parks Knowledge Dataset
        metrics:
          - type: accuracy
            value: 94.7
            name: Information Accuracy
          - type: bleu
            value: 0.85
            name: Response Quality (BLEU)
          - type: rouge-l
            value: 0.82
            name: Content Relevance (ROUGE-L)
          - type: response_time
            value: 180
            name: Average Response Time (ms)
          - type: knowledge_coverage
            value: 98.2
            name: Park Coverage Percentage