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RichardErkhov/
yoonusajward01_-_triptuner-gguf

Name: yoonusajward01_-_triptuner-gguf
Availability: InStock

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triptuner - GGUF

Model creator: https://huggingface.co/yoonusajward01/
Original model: https://huggingface.co/yoonusajward01/triptuner/

Name	Quant method	Size
triptuner.Q2_K.gguf	Q2_K	0.08GB
triptuner.IQ3_XS.gguf	IQ3_XS	0.08GB
triptuner.IQ3_S.gguf	IQ3_S	0.08GB
triptuner.Q3_K_S.gguf	Q3_K_S	0.08GB
triptuner.IQ3_M.gguf	IQ3_M	0.09GB
triptuner.Q3_K.gguf	Q3_K	0.09GB
triptuner.Q3_K_M.gguf	Q3_K_M	0.09GB
triptuner.Q3_K_L.gguf	Q3_K_L	0.1GB
triptuner.IQ4_XS.gguf	IQ4_XS	0.1GB
triptuner.Q4_0.gguf	Q4_0	0.1GB
triptuner.IQ4_NL.gguf	IQ4_NL	0.1GB
triptuner.Q4_K_S.gguf	Q4_K_S	0.1GB
triptuner.Q4_K.gguf	Q4_K	0.11GB
triptuner.Q4_K_M.gguf	Q4_K_M	0.11GB
triptuner.Q4_1.gguf	Q4_1	0.11GB
triptuner.Q5_0.gguf	Q5_0	0.11GB
triptuner.Q5_K_S.gguf	Q5_K_S	0.11GB
triptuner.Q5_K.gguf	Q5_K	0.12GB
triptuner.Q5_K_M.gguf	Q5_K_M	0.12GB
triptuner.Q5_1.gguf	Q5_1	0.12GB
triptuner.Q6_K.gguf	Q6_K	0.13GB
triptuner.Q8_0.gguf	Q8_0	0.17GB

Original model description:

library_name: transformers tags:

gpt2
transformers
text-generation
adventure
travel-itinerary
custom-model pipeline_tag: text-generation

TripTuner: Adventure Travel Itinerary Generator for Central Province, Sri Lanka

Overview

TripTuner is a custom-trained GPT-2 model designed to generate personalized adventure travel itineraries specifically for locations within the Central Province of Sri Lanka. The model is fine-tuned to provide detailed descriptions of adventure activities such as trekking, hiking, camping, and exploring scenic views, making it ideal for travel enthusiasts and tour planners.

Model Details

Model Type: GPT-2
Library: Transformers by Hugging Face
Use Case: Text generation focused on adventure travel itineraries
Languages: English
Base Model: GPT-2
Training Data: The model was fine-tuned on a custom dataset of adventure activities in various locations within Sri Lanka's Central Province.

How to Use

You can use this model directly with the Hugging Face Inference API or load it into your Python environment using the Transformers library.

Using the Inference API

You can test the model directly via the Hugging Face platform by clicking on the "Inference API" tab.

Using Transformers Pipeline

python

from transformers import pipeline

# Load the text generation pipeline using the uploaded model from Hugging Face
generator = pipeline('text-generation', model='yoonusajward01/triptuner')

# Test the model with a prompt
response = generator("[Q] Describe an adventure itinerary for Knuckles Mountain Range.", max_length=150, num_return_sequences=1, truncation=True)
print(response[0]['generated_text'])

RichardErkhov/yoonusajward01_-_triptuner-gguf