Norod78_-_distilgpt2-base-pretrained-he-4bits

Quantization made by Richard Erkhov.

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distilgpt2-base-pretrained-he - bnb 4bits

• Model creator: https://huggingface.co/Norod78/
• Original model: https://huggingface.co/Norod78/distilgpt2-base-pretrained-he/

Original model description:

language: he

thumbnail: https://avatars1.githubusercontent.com/u/3617152?norod.jpg widget:

• text: "האיש האחרון עלי אדמות ישב לבד בחדרו כשלפתע נשמעה נקישה"
• text: "שלום, קרואים לי"
• text: "הארי פוטר חייך חיוך נבוך"
• text: "החתול שלך מאוד חמוד ו"

license: mit

distilgpt2-base-pretrained-he

A tiny GPT2 based Hebrew text generation model initially trained on a TPUv3-8 which was made avilable to me via the TPU Research Cloud Program. Then was further fine-tuned on GPU.

Dataset

oscar (unshuffled deduplicated he) - Homepage | Dataset Permalink

The Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture.

CC-100 (he) - HomePage

This corpus comprises of monolingual data for 100+ languages and also includes data for romanized languages. This was constructed using the urls and paragraph indices provided by the CC-Net repository by processing January-December 2018 Commoncrawl snapshots. Each file comprises of documents separated by double-newlines and paragraphs within the same document separated by a newline. The data is generated using the open source CC-Net repository.

Misc

• Hebrew Twitter
• Wikipedia
• Various other sources

Training

• Done on a TPUv3-8 VM using Huggingface's clm-flax example script <BR>
• I have made a list of items which might make it easier for other to use this script. The list was posted to This discussion forum
• Further training was performed on GPU

Usage

Simple usage sample code

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

def main():
    model_name="Norod78/distilgpt2-base-pretrained-he"

    prompt_text = "שלום, קוראים לי"
    generated_max_length = 192

    print("Loading model...")
    model =  AutoModelForCausalLM.from_pretrained(model_name)
    print('Loading Tokenizer...')
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)

    print("Generating text...")
    result = text_generator(prompt_text, num_return_sequences=1, batch_size=1, do_sample=True, top_k=40, top_p=0.92, temperature = 1, repetition_penalty=5.0, max_length = generated_max_length)

    print("result = " + str(result))

if __name__ == '__main__':
    main()