Add application file

app.py

@@ -0,0 +1,66 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
+from threading import Thread
+
+import torch, transformers
+import sys, os
+sys.path.append(
+    os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)))
+from transformers import AutoModelForCausalLM,AutoTokenizer,LlamaTokenizer
+
+print("Creat tokenizer...")
+tokenizer = LlamaTokenizer.from_pretrained('IEITYuan/Yuan2-2B-hf', add_eos_token=False, add_bos_token=False, eos_token='<eod>')
+tokenizer.add_tokens(['<sep>', '<pad>', '<mask>', '<predict>', '<FIM_SUFFIX>', '<FIM_PREFIX>', '<FIM_MIDDLE>','<commit_before>','<commit_msg>','<commit_after>','<jupyter_start>','<jupyter_text>','<jupyter_code>','<jupyter_output>','<empty_output>'], special_tokens=True)
+
+print("Creat model...")
+model = AutoModelForCausalLM.from_pretrained('IEITYuan/Yuan2-2B-hf', device_map='auto', torch_dtype=torch.bfloat16, trust_remote_code=True)
+
+
+# Defining a custom stopping criteria class for the model's text generation.
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        stop_ids = [2]  # IDs of tokens where the generation should stop.
+        for stop_id in stop_ids:
+            if input_ids[0][-1] == stop_id:  # Checking if the last generated token is a stop token.
+                return True
+        return False
+
+
+# Function to generate model predictions.
+def predict(message, history):
+    history_transformer_format = history + [[message, ""]]
+    stop = StopOnTokens()
+
+    # Formatting the input for the model.
+    messages = "</s>".join(["</s>".join(["\n<|user|>:" + item[0], "\n<|assistant|>:" + item[1]])
+                        for item in history_transformer_format])
+    model_inputs = tokenizer([messages], return_tensors="pt").to(device)
+    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=1024,
+        do_sample=True,
+        top_p=0.95,
+        top_k=50,
+        temperature=0.7,
+        num_beams=1,
+        stopping_criteria=StoppingCriteriaList([stop])
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()  # Starting the generation in a separate thread.
+    partial_message = ""
+    for new_token in streamer:
+        partial_message += new_token
+        if '</s>' in partial_message:  # Breaking the loop if the stop token is generated.
+            break
+        yield partial_message
+
+
+# Setting up the Gradio chat interface.
+gr.ChatInterface(predict,
+                 title="Yuan2_2b_chatBot",
+                 description="请提问",
+                 examples=['请问目前最先进的机器学习算法有哪些？']
+                 ).launch()  # Launching the web interface.