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Running
on
Zero
| from transformers import pipeline, TextIteratorStreamer | |
| import torch | |
| from threading import Thread | |
| import gradio as gr | |
| import spaces | |
| import re | |
| model_id = "openai/gpt-oss-20b" | |
| pipe = pipeline( | |
| "text-generation", | |
| model=model_id, | |
| torch_dtype="auto", | |
| device_map="auto", | |
| ) | |
| def format_conversation_history(chat_history): | |
| messages = [] | |
| for item in chat_history: | |
| role = item["role"] | |
| content = item["content"] | |
| if isinstance(content, list): | |
| content = content[0]["text"] if content and "text" in content[0] else str(content) | |
| messages.append({"role": role, "content": content}) | |
| return messages | |
| def generate_response(input_data, chat_history, max_new_tokens, system_prompt, temperature, top_p, top_k, repetition_penalty): | |
| new_message = {"role": "user", "content": input_data} | |
| system_message = [{"role": "system", "content": system_prompt}] if system_prompt else [] | |
| processed_history = format_conversation_history(chat_history) | |
| messages = system_message + processed_history + [new_message] | |
| streamer = TextIteratorStreamer(pipe.tokenizer, skip_prompt=True, skip_special_tokens=True) | |
| generation_kwargs = { | |
| "max_new_tokens": max_new_tokens, | |
| "do_sample": True, | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "top_k": top_k, | |
| "repetition_penalty": repetition_penalty, | |
| "streamer": streamer | |
| } | |
| thread = Thread(target=pipe, args=(messages,), kwargs=generation_kwargs) | |
| thread.start() | |
| #streaming try #1 | |
| buffer = "" | |
| full_response = "" | |
| for chunk in streamer: | |
| buffer += chunk | |
| parts = re.split(r'(\s+)', buffer) | |
| if re.match(r'\s+', parts[-1]) is not None: | |
| to_append = ''.join(parts) | |
| buffer = "" | |
| else: | |
| to_append = ''.join(parts[:-1]) | |
| buffer = parts[-1] | |
| if to_append: | |
| full_response += to_append | |
| yield full_response | |
| if buffer: | |
| full_response += buffer | |
| yield full_response | |
| demo = gr.ChatInterface( | |
| fn=generate_response, | |
| additional_inputs=[ | |
| gr.Slider(label="Max new tokens", minimum=64, maximum=4096, step=1, value=2048), | |
| gr.Textbox( | |
| label="System Prompt", | |
| value="You are a helpful assistant. Reasoning: medium", | |
| lines=4, | |
| placeholder="Change system prompt" | |
| ), | |
| gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, step=0.1, value=0.7), | |
| gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=0.9), | |
| gr.Slider(label="Top-k", minimum=1, maximum=100, step=1, value=50), | |
| gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.0) | |
| ], | |
| examples=[ | |
| [{"text": "Explain Newton laws clearly and concisely"}], | |
| [{"text": "Write a Python function to calculate the Fibonacci sequence"}], | |
| [{"text": "What are the benefits of open weight AI models"}], | |
| ], | |
| cache_examples=False, | |
| type="messages", | |
| description=""" | |
| # gpt-oss-20b | |
| Wait couple of seconds initially. You can adjust reasoning level in the system prompt like "Reasoning: high. | |
| """, | |
| fill_height=True, | |
| textbox=gr.Textbox( | |
| label="Query Input", | |
| placeholder="Type your prompt" | |
| ), | |
| stop_btn="Stop Generation", | |
| multimodal=False, | |
| theme=gr.themes.Soft() | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch(share=True) |