be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.# Make sure the model path is correct for your system!llm = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",    n_gpu_layers=n_gpu_layers,    n_batch=n_batch,    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls    callback_manager=callback_manager,    verbose=True, # Verbose is required to pass to the callback manager    grammar_path="/Users/rlm/Desktop/Code/langchain-main/langchain/libs/langchain/langchain/llms/grammars/json.gbnf",)result=llm("Describe a person in JSON format:")    {      "name": "John Doe",      "age": 34,      "": {        "title": "Software Developer",        "company": "Google"      },      "interests": [        "Sports",        "Music",        "Cooking"      ],      "address": {        "street_number": 123,        "street_name": "Oak Street",        "city": "Mountain View",        "state": "California",        "postal_code": 94040      }}        llama_print_timings:        load time =   357.51 ms    llama_print_timings:      sample time =  1213.30 ms /   144 runs   (    8.43 ms per token,   118.68 tokens per second)    llama_print_timings: prompt eval time =   356.78 ms /     9 tokens (   39.64 ms per token,    25.23 tokens per second)    llama_print_timings:        eval time =  3947.16 ms /   143 runs   (   27.60 ms per token,    36.23 tokens per second)    llama_print_timings:       total time =  5846.21 msWe can also supply list.gbnf to return a list:n_gpu_layers = 1 n_batch = 512llm = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",    n_gpu_layers=n_gpu_layers,    n_batch=n_batch,    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls    callback_manager=callback_manager,    verbose=True,
llama-cpp-python is a Python binding for llama.cpp.
llama-cpp-python is a Python binding for llama.cpp. ->: be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.# Make sure the model path is correct for your system!llm = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",    n_gpu_layers=n_gpu_layers,    n_batch=n_batch,    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls    callback_manager=callback_manager,    verbose=True, # Verbose is required to pass to the callback manager    grammar_path="/Users/rlm/Desktop/Code/langchain-main/langchain/libs/langchain/langchain/llms/grammars/json.gbnf",)result=llm("Describe a person in JSON format:")    {      "name": "John Doe",      "age": 34,      "": {        "title": "Software Developer",        "company": "Google"      },      "interests": [        "Sports",        "Music",        "Cooking"      ],      "address": {        "street_number": 123,        "street_name": "Oak Street",        "city": "Mountain View",        "state": "California",        "postal_code": 94040      }}        llama_print_timings:        load time =   357.51 ms    llama_print_timings:      sample time =  1213.30 ms /   144 runs   (    8.43 ms per token,   118.68 tokens per second)    llama_print_timings: prompt eval time =   356.78 ms /     9 tokens (   39.64 ms per token,    25.23 tokens per second)    llama_print_timings:        eval time =  3947.16 ms /   143 runs   (   27.60 ms per token,    36.23 tokens per second)    llama_print_timings:       total time =  5846.21 msWe can also supply list.gbnf to return a list:n_gpu_layers = 1 n_batch = 512llm = LlamaCpp(    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",    n_gpu_layers=n_gpu_layers,    n_batch=n_batch,    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls    callback_manager=callback_manager,    verbose=True,