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| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| base_model_id = "google/gemma-2b-it" | |
| adapter_model_id = "MatclassAI/Matclass-Gemma-2b-v1" | |
| print("Loading base tokenizer...") | |
| tokenizer = AutoTokenizer.from_pretrained(adapter_model_id) | |
| print("Loading base framework layers...") | |
| model = AutoModelForCausalLM.from_pretrained( | |
| base_model_id, | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| low_cpu_mem_usage=True | |
| ) | |
| print("Injecting custom fine-tuned adapters into core matrices...") | |
| model.load_adapter(adapter_model_id) | |
| print("Model infrastructure fully compiled!") | |
| def generate_text(prompt, system_prompt, temperature, max_tokens): | |
| if system_prompt: | |
| formatted_prompt = f"<start_of_turn>user\n{system_prompt}\n\n{prompt}<end_of_turn>\n<start_of_turn>model\n" | |
| else: | |
| formatted_prompt = f"<start_of_turn>user\n{prompt}<end_of_turn>\n<start_of_turn>model\n" | |
| inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device) | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=max_tokens, | |
| do_sample=True, | |
| temperature=temperature, | |
| top_k=50, | |
| top_p=0.95 | |
| ) | |
| generated = tokenizer.decode(outputs[0], skip_special_tokens=False) | |
| if formatted_prompt in generated: | |
| return generated.split("<start_of_turn>model\n")[-1].replace("<end_of_turn>", "").strip() | |
| return generated | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# Matclass AI - Gemma 2B Adapter Workspace") | |
| gr.Markdown("Dynamic LoRA adapter execution layer tracking custom weights.") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| system_input = gr.Textbox( | |
| label="System Instructions", | |
| placeholder="Define assistant behavior or context here...", | |
| lines=2 | |
| ) | |
| user_input = gr.Textbox( | |
| label="Query", | |
| placeholder="Ask anything...", | |
| lines=5 | |
| ) | |
| submit_btn = gr.Button("Generate Text", variant="primary") | |
| with gr.Column(scale=1): | |
| gr.Markdown("### Generation Hyperparameters") | |
| temp_slider = gr.Slider( | |
| minimum=0.1, | |
| maximum=1.5, | |
| value=0.7, | |
| step=0.05, | |
| label="Temperature" | |
| ) | |
| tokens_slider = gr.Slider( | |
| minimum=64, | |
| maximum=1024, | |
| value=256, | |
| step=64, | |
| label="Max New Tokens" | |
| ) | |
| with gr.Row(): | |
| output_text = gr.Textbox(label="Model Output", lines=8, interactive=False) | |
| submit_btn.click( | |
| fn=generate_text, | |
| inputs=[user_input, system_input, temp_slider, tokens_slider], | |
| outputs=output_text | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |