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app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
+from threading import Thread
+
+tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-instruct", trust_remote_code=True, device_map="auto")
+
+streamer = TextStreamer(tokenizer, skip_prompt=True)
+
+#streaming output
+def respond(message, history):
+    messages=[
+    { 'role': 'user', 'content': message}]
+    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+# 32021 is the id of <|EOT|> token
+    outputs = model.generate(inputs, streamer=streamer, max_new_tokens=1024, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
+    out_answer = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
+    generated_text = ""
+    for new_text in out_answer:
+      generated_text += new_text
+      yield generated_text
+
+
+demo_chatbot = gr.ChatInterface(respond, title="Deepseek-Coder", description="Enter text to start chatting.")
+
+demo_chatbot.launch()
+#https://huggingface.co/docs/transformers/internal/generation_utils

requirement.txt

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+accelerate
+gradio
+huggingface-hub
+torch
+transformers