Create app.py

app.py

@@ -0,0 +1,81 @@
+import time
+
+import gradio as gr
+import spaces
+from threading import Thread
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, BitsAndBytesConfig
+import torch
+
+MAX_INPUT_LIMIT = 3584
+
+MODEL_NAME = "Azure99/blossom-v5-9b"
+
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+GENERATE_CONFIG = dict(
+    max_new_tokens=1536,
+    temperature=0.5,
+    top_p=0.85,
+    top_k=50,
+    repetition_penalty=1.05
+)
+
+
+def get_input_ids(inst, history):
+    prefix = ("A chat between a human and an artificial intelligence bot. "
+              "The bot gives helpful, detailed, and polite answers to the human's questions.")
+    patterns = []
+    for conv in history:
+        patterns.append(f'\n|Human|: {conv[0]}\n|Bot|: ')
+        patterns.append(f'{conv[1]}')
+    patterns.append(f'\n|Human|: {inst}\n|Bot|: ')
+    patterns[0] = prefix + patterns[0]
+
+    input_ids = []
+    for i, pattern in enumerate(patterns):
+        input_ids += tokenizer.encode(pattern, add_special_tokens=(i == 0))
+        if i % 2 == 1:
+            input_ids += [tokenizer.eos_token_id]
+    return input_ids
+
+
+@spaces.GPU
+def chat(inst, history):
+    with torch.no_grad():
+        streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+        input_ids = get_input_ids(inst, history)
+        print(len(input_ids))
+        if len(input_ids) > MAX_INPUT_LIMIT:
+            yield "The input is too long, please clear the history."
+            return
+        generation_kwargs = dict(input_ids=torch.tensor([input_ids]).to(model.device), do_sample=True,
+                                 streamer=streamer, **GENERATE_CONFIG)
+        Thread(target=model.generate, kwargs=generation_kwargs).start()
+        
+        # stop watch
+        start = time.time()
+        outputs = ""
+        for new_text in streamer:
+            outputs += new_text
+            yield outputs
+        total_time = time.time() - start
+        output_token_len = len(tokenizer.encode(outputs, add_special_tokens=False))
+        speed = output_token_len / total_time
+        print(f"Speed: {speed:.2f} tokens/s")
+
+
+gr.ChatInterface(chat,
+                 chatbot=gr.Chatbot(show_label=False, height=500, show_copy_button=True, render_markdown=True),
+                 textbox=gr.Textbox(placeholder="", container=False, scale=7),
+                 title="Blossom Demo",
+                 description='Hello, I am Blossom, an open source conversational large language model.🌠'
+                             '<a href="https://github.com/Azure99/BlossomLM">GitHub</a>',
+                 theme="soft",
+                 examples=["Hello", "What is MBTI", "用Python实现二分查找", "为switch写一篇小红书种草文案，带上emoji"],
+                 clear_btn="🗑️Clear",
+                 undo_btn="↩️Undo",
+                 retry_btn="🔄Retry",
+                 submit_btn="➡️Submit",
+                 ).queue().launch()