Initial commit

app.py

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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()

requirements.txt

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+transformers
+pandas
+torch
+gradio
+accelerate
+openpyxl