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Running on Zero
Running on Zero
| import gradio as gr | |
| import spaces | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_id = "mistralai/Mistral-Small-Instruct-2409" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype="auto", | |
| device_map="auto" | |
| ) | |
| def respond(message, history): | |
| inputs = tokenizer(message, return_tensors="pt").to("cuda") | |
| inputs_size = len(inputs.input_ids[0]) | |
| response = model.generate( | |
| **inputs, | |
| max_new_tokens=256, | |
| do_sample=True, | |
| temperature=0.2, | |
| top_p=0.9, | |
| repetition_penalty=1.5, | |
| eos_token_id=tokenizer.eos_token_id, | |
| ) | |
| output = tokenizer.decode(response[0][inputs_size:], skip_special_tokens=True) | |
| return output | |
| app = gr.ChatInterface(fn=respond, title="Simple Chat") | |
| if __name__ == "__main__": | |
| app.launch() |