Create app.py

app.py

@@ -0,0 +1,69 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from threading import Thread
+import gradio as gr
+import torch
+
+MAX_INPUT_TOKEN_LENGTH = 4096
+
+model_id = 'HuggingFaceH4/zephyr-7b-beta'
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer.use_default_system_prompt = False
+
+def generate(input, chat_history=[], system_prompt=False, max_new_tokens=512, temperature=0.5, top_p=0.95, top_k=50, repetition_penalty=1.2):
+    conversation = []
+    if system_prompt:
+        conversation.append({
+            'role': 'system',
+            'content': system_prompt
+        })
+    for user, assistant in chat_history:
+        conversation.extend({
+            'role': 'user',
+            'content': user
+        },
+        {
+            'role': 'assistant',
+            'content': assistant
+        })
+    conversation.append({
+        'role': 'user',
+        'content': input
+    })
+
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors='pt')
+    if input_ids.shape[1] > MAXX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to(model.device)
+
+    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        {'input_ids': input_ids},
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield ''.join(outputs)
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    examples=[
+        'What is GPT?',
+        'What is Life?',
+        'Who is Alan Turing'
+    ]
+)
+
+chat_interface.queue(max_size=20).launch()