Uploaded model
- Developed by: Dragneel
- License: apache-2.0
- Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit
Use The Model
from transformers import AutoTokenizer, AutoModelForCausalLM
Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16")
model = AutoModelForCausalLM.from_pretrained("Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16")
Example input text
input_text = "Summarize Nepali Text in Nepali: काठमाडौंको बहिराव बसपार्कमा एक भयानक दुर्घटना घटेको थियो। रातको समय थियो र भारी बर्फ जम्मा भएको थियो।"
Tokenize the input text
input_ids = tokenizer.encode(input_text, return_tensors='pt')
Generate text with adjusted parameters
outputs = model.generate(input_ids, max_new_tokens=50)
Decode the generated tokens
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
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