| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| model_id = "hf-100/mistral-spellbound-research" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=True) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_id, | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| use_auth_token=True | |
| ) | |
| def generate(prompt): | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| output = model.generate( | |
| **inputs, | |
| max_new_tokens=300, | |
| temperature=0.8, | |
| top_p=0.95, | |
| do_sample=True | |
| ) | |
| return tokenizer.decode(output[0], skip_special_tokens=True) | |
| iface = gr.Interface( | |
| fn=generate, | |
| inputs=gr.Textbox(lines=4, placeholder="Enter your prompt..."), | |
| outputs="text", | |
| title="Spellbound Model - Roleplay AI", | |
| description="Powered by hf-100/mistral-spellbound-research" | |
| ) | |
| iface.launch() |