Dragneel
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Phi-3-mini-Nepali-Text-Summarization-f16

Text Generation

text-generation-inference

text2text-generation

Model card Files Files and versions

YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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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Model tree for Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16

Base model

unsloth/Phi-3-mini-4k-instruct-bnb-4bit

Finetuned

(635)

this model

Dataset used to train Dragneel/Phi-3-mini-Nepali-Text-Summarization-f16