Upload app.py

app.py

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+import torch
+from peft import PeftModel
+import transformers
+import gradio as gr
+from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
+from transformers import Trainer
+
+BASE_MODEL = "TheBloke/stable-vicuna-13B-HF"
+model = LlamaForCausalLM.from_pretrained(BASE_MODEL, load_in_8bit=True, torch_dtype=torch.float16, device_map="auto")
+tokenizer = LlamaTokenizer.from_pretrained(BASE_MODEL)
+tokenizer.pad_token_id = 0
+tokenizer.padding_side = "left"
+
+def format_prompt(prompt: str) -> str:
+    return f"### Human: {prompt}\n### Assistant:"
+
+generation_config = GenerationConfig(
+    max_new_tokens=128,
+    temperature=0.2,
+    repetition_penalty=1.0,
+)
+
+def generate_text(prompt: str):
+    formatted_prompt = format_prompt(prompt)
+
+    inputs = tokenizer(
+        formatted_prompt, 
+        padding=False, 
+        add_special_tokens=False, 
+        return_tensors="pt"
+    ).to(model.device)
+
+    with torch.inference_mode():
+        tokens = model.generate(**inputs, generation_config=generation_config)
+
+    response = tokenizer.decode(tokens[0], skip_special_tokens=True)
+    assistant_index = response.find("### Assistant:") + len("### Assistant:")
+    return response[assistant_index:].strip()
+
+iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
+iface.launch()
+