Update README.md

README.md

@@ -84,7 +84,48 @@ Refer to the [original model card](https://huggingface.co/MLP-KTLim/llama-3-Kore
 
 
 ## Example code
+'''python
+!CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python
+!huggingface-cli download MLP-KTLim/llama-3-Korean-Bllossom-8B-4bit --local-dir='YOUR-LOCAL-FOLDER-PATH'
+
+from llama_cpp import Llama
+from transformers import AutoTokenizer
+
+model_id = 'MLP-KTLim/llama-3-Korean-Bllossom-8B-4bit'
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = Llama(
+    model_path='YOUR-LOCAL-FOLDER-PATH/llama-3-Korean-Bllossom-8B-Q4_K_M.gguf',
+    n_ctx=512,
+    n_gpu_layers=-1        # Number of model layers to offload to GPU
+)
+
+PROMPT = \
+'''당신은 유용한 AI 어시스턴트입니다. 사용자의 질의에 대해 친절하고 정확하게 답변해야 합니다.
+You are a helpful AI assistant, you'll need to answer users' queries in a friendly and accurate manner.'''
+
+instruction = 'Your Instruction'
+
+messages = [
+    {"role": "system", "content": f"{PROMPT}"},
+    {"role": "user", "content": f"{instruction}"}
+    ]
+
+prompt = tokenizer.apply_chat_template(
+    messages, 
+    tokenize = False,
+    add_generation_prompt=True
+)
+
+generation_kwargs = {
+    "max_tokens":512,
+    "stop":["<|eot_id|>"],
+    "echo":True, # Echo the prompt in the output
+    "top_k":1 # This is essentially greedy decoding, since the model will always return the highest-probability token. Set this value > 1 for sampling decoding
+}
 
+resonse_msg = model(prompt, **generation_kwargs)
+print(resonse_msg['choices'][0]['text'][len(prompt):])
+'''