Text Generation
Transformers
TensorBoard
Safetensors
gpt2
adventure
travel-itinerary
custom-model
text-generation-inference
Instructions to use yoonusajward01/triptuner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoonusajward01/triptuner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yoonusajward01/triptuner")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yoonusajward01/triptuner") model = AutoModelForCausalLM.from_pretrained("yoonusajward01/triptuner") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yoonusajward01/triptuner with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yoonusajward01/triptuner" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yoonusajward01/triptuner", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yoonusajward01/triptuner
- SGLang
How to use yoonusajward01/triptuner 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 "yoonusajward01/triptuner" \ --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": "yoonusajward01/triptuner", "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 "yoonusajward01/triptuner" \ --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": "yoonusajward01/triptuner", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yoonusajward01/triptuner with Docker Model Runner:
docker model run hf.co/yoonusajward01/triptuner
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README.md
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- **Library**: Transformers by Hugging Face
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- **Use Case**: Text generation focused on adventure travel itineraries
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- **Languages**: English
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- **Training Data**: The model was fine-tuned on a custom dataset of adventure activities in various locations within Sri Lanka's Central Province.
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## How to Use
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generator = pipeline('text-generation', model='yoonusajward01/triptuner')
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# Test the model with a prompt
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response = generator("[Q] Describe an adventure itinerary for Knuckles Mountain Range.", max_length=150, num_return_sequences=1)
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print(response[0]['generated_text'])
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- **Library**: Transformers by Hugging Face
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- **Use Case**: Text generation focused on adventure travel itineraries
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- **Languages**: English
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- **Base Model**: GPT-2
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- **Training Data**: The model was fine-tuned on a custom dataset of adventure activities in various locations within Sri Lanka's Central Province.
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## How to Use
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generator = pipeline('text-generation', model='yoonusajward01/triptuner')
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# Test the model with a prompt
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response = generator("[Q] Describe an adventure itinerary for Knuckles Mountain Range.", max_length=150, num_return_sequences=1, truncation=True)
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print(response[0]['generated_text'])
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