import gradio as gr from threading import Thread from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, TextIteratorStreamer model_id = "rasyosef/gpt2-medium-amharic-28k-512" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) gpt2_am = pipeline( "text-generation", model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id ) def generate(prompt): prompt_length = len(tokenizer.tokenize(prompt)) if prompt_length >= 128: yield prompt + "\n\nPrompt is too long. It needs to be less than 128 tokens." else: max_new_tokens = max(0, 128 - prompt_length) streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=False, skip_special_tokens=True, timeout=300.0) thread = Thread( target=gpt2_am, kwargs={ "text_inputs": prompt, "max_new_tokens": max_new_tokens, "temperature": 0.4, "do_sample": True, "top_k": 8, "top_p": 0.8, "repetition_penalty": 1.4, "streamer": streamer }) thread.start() generated_text = "" for word in streamer: generated_text += word response = generated_text.strip() yield response with gr.Blocks(css="#prompt_textbox textarea {color: blue}") as demo: gr.Markdown(""" # GPT2 Amharic This is a demo for a smaller version of OpenAI's [gpt2](https://huggingface.co/openai-community/gpt2) decoder transformer model pretrained for 2 days on `290 million` tokens of **Amharic** text. The context size of [gpt2-medium-amharic](https://huggingface.co/rasyosef/gpt2-medium-amharic-28k-512) is 512 tokens. This is a base model and hasn't undergone any supervised finetuing yet. Please **enter a prompt** and click the **Generate** button to generate completions for the prompt. #### Text generation parameters: - `temperature` : **0.4** - `do_sample` : **True** - `top_k` : **8** - `top_p` : **0.8** - `repetition_penalty` : **1.4** """) prompt = gr.Textbox(label="Prompt", placeholder="Enter prompt here", lines=4, interactive=True, elem_id="prompt_textbox") with gr.Row(): with gr.Column(): gen = gr.Button("Generate") with gr.Column(): btn = gr.ClearButton([prompt]) gen.click(generate, inputs=[prompt], outputs=[prompt]) examples = gr.Examples( examples=[ "አዲስ አበባ", "በእንግሊዙ ፕሬሚየር ሊግ", "ፕሬዚዳንት ዶናልድ ትራምፕ", "በመስቀል አደባባይ" ], inputs=[prompt], ) demo.queue().launch(debug=True)