Create main.py

main.py

@@ -0,0 +1,51 @@
+import gradio as gr
+from transformers import TextIteratorStreamer
+from threading import Thread
+from transformers import StoppingCriteria, StoppingCriteriaList
+import torch
+
+model_name = "microsoft/Phi-3-mini-128k-instruct"
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map='cuda')
+
+
+model = model.to('cuda:0')
+
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [29, 0]
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:
+                return True
+        return False
+@spaces.GPU(duration=180)
+def predict(message, history):
+    history_transformer_format = history + [[message, ""]]
+    stop = StopOnTokens()
+    messages = "".join(["".join(["<|end|>\n<|user|>\n"+item[0], "<|end|>\n<|assistant|>\n"+item[1]]) for item in history_transformer_format])
+    model_inputs = tokenizer([messages], return_tensors="pt").to("cuda")
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=4096,
+        do_sample=True,
+        top_p=0.9,
+        top_k=40,
+        temperature=0.9,
+        num_beams=1,
+        stopping_criteria=StoppingCriteriaList([stop])
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    partial_message = ""
+    for new_token in streamer:
+        if new_token != '<':
+            partial_message += new_token
+        yield partial_message
+
+
+demo = gr.ChatInterface(fn=predict, examples=["What is life?"], title="AI", fill_height=True)
+
+demo.launch(show_api=False)