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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import transformers | |
| model_name = "Azurro/APT3-1B-Instruct-v1" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| def generate_text(input_text, max_tokens, temperature, top_p): | |
| sequences = pipeline(max_new_tokens=max_tokens, temperature=temperature, top_p=top_p, eos_token_id=tokenizer.eos_token_id, text_inputs=input_text) | |
| txt = "" | |
| for seq in sequences: | |
| txt += seq['generated_text'] | |
| return txt | |
| models_list = ["Azurro/APT3-1B-Instruct-v1"] | |
| iface = gr.Interface( | |
| fn=generate_text, | |
| inputs=[ | |
| gr.Textbox(lines=2, placeholder="Wpisz tekst tutaj...", label="Wpisz tekst"), | |
| gr.Slider(value=500, label="Maksymalna długość", step=1, minimum=1, maximum=4000), | |
| gr.Slider(label="Temperatura", minimum=0.0, maximum=2.0, step=0.01, value=1.0), | |
| gr.Number(value=1.0, label="Top P", step=0.01, minimum=0.0, maximum=2.0) | |
| ], | |
| outputs=gr.Textbox(label="Wygenerowany tekst") | |
| ) | |
| iface.launch() | |