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| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, logging | |
| import gradio as gr | |
| model_name = "microsoft/phi-2" | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| trust_remote_code=True | |
| ) | |
| model.config.use_cache = False | |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| # Loading adapter (trained LORA weights) | |
| # ckpt = '/content/drive/MyDrive/S27/results/checkpoint-500' | |
| # model.load_adapter(ckpt) | |
| adapter_path = 'checkpoint-500' | |
| model.load_adapter(adapter_path) | |
| def inference(prompt): | |
| pipe = pipeline(task="text-generation",model=model,tokenizer=tokenizer,max_length = 100) | |
| result = pipe(f"<s>[INST] {prompt} [/INST]") | |
| return result[0]['generated_text'] | |
| INTERFACE = gr.Interface(fn=inference, inputs=[gr.Textbox(label= "Prompt", value= 'what should we do to save time')], | |
| outputs=gr.Text(label= "Generated Text"), title="Language Model Phi-2 fine-tuned with OpenAssistant/oasst-1 dataset using QLoRA strategy", | |
| examples = [['explain transpiration in plants'],] | |
| ).launch(debug=True) |