Create app.py

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+import transformers
+from torch import bfloat16
+# from dotenv import load_dotenv  # if you wanted to adapt this for a repo that uses auth
+from threading import Thread
+
+
+#HF_AUTH = os.getenv('HF_AUTH')
+model_id = "stabilityai/StableBeluga-7B"
+
+bnb_config = transformers.BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=bfloat16
+)
+model_config = transformers.AutoConfig.from_pretrained(
+    model_id,
+    #use_auth_token=HF_AUTH
+)
+
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    config=model_config,
+    quantization_config=bnb_config,
+    device_map='auto',
+    #use_auth_token=HF_AUTH
+)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(
+    model_id,
+    #use_auth_token=HF_AUTH
+)
+
+
+def prompt_build(system_prompt, user_inp, hist):
+    prompt = f"""### System:\n{system_prompt}\n\n"""
+    
+    for pair in hist:
+        prompt += f"""### User:\n{pair[0]}\n\n### Assistant:\n{pair[1]}\n\n"""
+
+    prompt += f"""### User:\n{user_inp}\n\n### Assistant:"""
+    return prompt
+
+def chat(user_input, history, system_prompt):
+
+    prompt = prompt_build(system_prompt, user_input, history)
+    model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
+
+    streamer = transformers.TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
+
+    generate_kwargs = dict(
+        model_inputs,
+        streamer=streamer,
+        max_new_tokens=2048,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.8,
+        top_k=50
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+
+    model_output = ""
+    for new_text in streamer:
+        model_output += new_text
+        yield model_output
+    return model_output
+
+
+with gr.Blocks() as demo:
+    system_prompt = gr.Textbox("You are helpful AI.", label="System Prompt")
+    chatbot = gr.ChatInterface(fn=chat, additional_inputs=[system_prompt])
+
+demo.queue().launch()