| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig,BitsAndBytesConfig | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0", trust_remote_code=True) | |
| tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0", trust_remote_code=True) | |
| generation_config = GenerationConfig( | |
| penalty_alpha=0.6, | |
| do_sample = True, | |
| top_k=5, | |
| temperature=0.5, | |
| repetition_penalty=1.2, | |
| max_new_tokens=500, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| def generate_text(input): | |
| input_text = f'<|system|>\n You are a chatbot who can help code!</s> \n <|user|> \n {input} \n <|assistant|> \n' | |
| input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device) | |
| output_ids = model.generate(input_ids, generation_config=generation_config) | |
| output_text = tokenizer.decode(output_ids[0],skip_special_tokens=True) | |
| return output_text | |
| iface = gr.Interface(fn=generate_text, inputs="text", outputs="text") | |
| iface.launch() | |