Upload app.py

app.py

@@ -0,0 +1,52 @@
+
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "zirui3/gpt_1.4B_oa_instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+chip_map= {
+    'gpt_neox.embed_in': 0,
+    'gpt_neox.layers': 0,
+    'gpt_neox.final_layer_norm': 0,
+    'embed_out': 0
+ }
+model = AutoModelForCausalLM.from_pretrained(name, device_map=chip_map, torch_dtype=torch.float16, load_in_8bit=True)
+#model = AutoModelForCausalLM.from_pretrained(model_name)
+
+
+def predict(input, history=[], MAX_NEW_TOKENS = 500):
+    text = "User: " + input + "\n\nChip: "
+    new_user_input_ids = tokenizer(text, return_tensors="pt").input_ids
+    # bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1).to("cuda")
+    bot_input_ids = torch.cat([torch.LongTensor(history), new_user_input_ids], dim=-1)
+
+    generated_ids = model.generate(bot_input_ids,
+        max_length=MAX_NEW_TOKENS, pad_token_id=tokenizer.eos_token_id,
+        do_sample=True,
+        top_p=0.95, temperature=0.5, penalty_alpha=0.6, top_k=4, repetition_penalty=1.03,
+        num_return_sequences=1)
+
+    response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+    history = generated_ids.tolist()
+
+    # convert to list of user & bot response
+    response = response.split("\n\n")
+    response_pairs = [(response[i], response[i+1]) for i in range(0, len(response)-1, 2)]
+    return response_pairs, history
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    state = gr.State([])
+
+    with gr.Row():
+        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
+
+    txt.submit(predict, [txt, state], [chatbot, state])
+
+
+if __name__ == "__main__":
+    # demo.launch(debug=True, server_name="0.0.0.0", server_port=9991)
+    demo.launch()