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kherud / java-llama.cpp

Java Bindings for llama.cpp - A Port of Facebook's LLaMA model in C/C++
MIT License
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model.generate aborts with "libc++abi: terminating due to uncaught exception of type std::length_error: basic_string" #29

Closed abadelt closed 5 months ago

abadelt commented 9 months ago

Hi, I built java-llama.cpp on my MacBook (M2 / 64 GB RAM), but when running my own code or one of the provided examples, the process aborts nearly every time with the following exception:

_libc++abi: terminating due to uncaught exception of type std::length_error: basicstring

I have the impression that it has to do with the overall number of tokens in the prompt and generated output, as it seems to happen earlier the longer the prompt is. Also, I did not encounter it for very short answers. (Btw: The model.complete method also aborts, the difference obviously being that I don't see anything of the answer before the process terminates.)

For example using the llama-2-13b-chat.Q4_0.gguf model and the following prompt, all was fine:

Prompt: [INST]<<SYS>>You are a helpful assistant answering questions to users precisely and truthfully.<</SYS>>
What is the biggest city in Asia?[/INST]
Output: The biggest city in Asia is Tokyo, Japan, with a population of over 38 million people.

But when asking for something more complex, I run into the error before the answer is complete (output is abbreviated):

Prompt: [INST]<<SYS>>You are a helpful assistant answering questions to users precisely and truthfully.<</SYS>>
Can you give a general description of Tokyo, its history and economic status, and add a few sightseeing tipps for European tourists?[/INST]
Output:
Of course! I'd be happy to help.
Tokyo is the capital and largest city of Japan, located [...] 
Sightseeing Tips for European Tourists:
[...]
7. Take a boat ride on the Sumida River to seelibc++abi: terminating due to uncaught exception of type std::length_error: basic_string

Below is a full log of the failing run, in case it helps. I was using the MainExample class from java-llama.cpp, only adapted the prompt setup a bit to the used model (which did not make a difference regarding the error).

/Users/andreas/.sdkman/candidates/java/21-tem/bin/java -javaagent:/Users/andreas/Library/Application Support/JetBrains/Toolbox/apps/IDEA-U/ch-0/232.9921.47/IntelliJ IDEA.app/Contents/lib/idea_rt.jar=63645:/Users/andreas/Library/Application Support/JetBrains/Toolbox/apps/IDEA-U/ch-0/232.9921.47/IntelliJ IDEA.app/Contents/bin -Dfile.encoding=UTF-8 -Dsun.stdout.encoding=UTF-8 -Dsun.stderr.encoding=UTF-8 -classpath /Users/andreas/Projekte/Playground/ai/java-llama.cpp/target/test-classes:/Users/andreas/Projekte/Playground/ai/java-llama.cpp/target/classes:/Users/andreas/.m2/repository/junit/junit/4.13.1/junit-4.13.1.jar:/Users/andreas/.m2/repository/org/hamcrest/hamcrest-core/1.3/hamcrest-core-1.3.jar:/Users/andreas/.m2/repository/org/jetbrains/annotations/24.0.1/annotations-24.0.1.jar examples.MainExample /de/kherud/llama/Mac/aarch64 Extracted 'ggml-metal.metal' to '/var/folders/vq/77pt2w8d0fs4gxvhvcg_q00000gn/T/ggml-metal.metal' Extracted 'libllama.dylib' to '/var/folders/vq/77pt2w8d0fs4__gxvhvcg_q00000gn/T/libllama.dylib' Extracted 'libjllama.dylib' to '/var/folders/vq/77pt2w8d0fs4gxvhvcg_q00000gn/T/libjllama.dylib' llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from /Users/andreas/Projekte/Playground/ai/llama.cpp/models/llama-2-13b-chat.Q4_0.gguf (version GGUF V2 (latest)) llama_model_loader: - tensor 0: token_embd.weight q4_0 [ 5120, 32000, 1, 1 ] llama_model_loader: - tensor 1: blk.0.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 2: blk.0.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 3: blk.0.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 4: blk.0.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 5: blk.0.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 6: blk.0.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 7: blk.0.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 8: blk.0.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 9: blk.0.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 10: blk.1.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 11: blk.1.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 12: blk.1.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 13: blk.1.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 14: blk.1.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 15: blk.1.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 16: blk.1.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 17: blk.1.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 18: blk.1.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 19: blk.10.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 20: blk.10.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 21: blk.10.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 22: blk.10.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 23: blk.10.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 24: blk.10.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 25: blk.10.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 26: blk.10.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 27: blk.10.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 28: blk.11.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 29: blk.11.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 30: blk.11.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 31: blk.11.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 32: blk.11.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 33: blk.11.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 34: blk.11.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 35: blk.11.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 36: blk.11.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 37: blk.12.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 38: blk.12.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 39: blk.12.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 40: blk.12.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 41: blk.12.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 42: blk.12.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 43: blk.12.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 44: blk.12.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 45: blk.12.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 46: blk.13.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 47: blk.13.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 48: blk.13.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 49: blk.13.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 50: blk.13.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 51: blk.13.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 52: blk.13.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 53: blk.13.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 54: blk.13.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 55: blk.14.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 56: blk.14.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 57: blk.14.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 58: blk.14.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 59: blk.14.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 60: blk.14.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 61: blk.14.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 62: blk.14.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 63: blk.14.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 64: blk.15.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 65: blk.15.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 66: blk.2.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 67: blk.2.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 68: blk.2.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 69: blk.2.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 70: blk.2.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 71: blk.2.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 72: blk.2.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 73: blk.2.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 74: blk.2.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 75: blk.3.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 76: blk.3.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 77: blk.3.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 78: blk.3.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 79: blk.3.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 80: blk.3.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 81: blk.3.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 82: blk.3.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 83: blk.3.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 84: blk.4.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 85: blk.4.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 86: blk.4.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 87: blk.4.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 88: blk.4.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 89: blk.4.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 90: blk.4.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 91: blk.4.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 92: blk.4.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 93: blk.5.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 94: blk.5.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 95: blk.5.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 96: blk.5.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 97: blk.5.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 98: blk.5.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 99: blk.5.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 100: blk.5.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 101: blk.5.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 102: blk.6.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 103: blk.6.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 104: blk.6.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 105: blk.6.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 106: blk.6.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 107: blk.6.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 108: blk.6.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 109: blk.6.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 110: blk.6.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 111: blk.7.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 112: blk.7.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 113: blk.7.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 114: blk.7.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 115: blk.7.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 116: blk.7.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 117: blk.7.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 118: blk.7.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 119: blk.7.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 120: blk.8.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 121: blk.8.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 122: blk.8.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 123: blk.8.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 124: blk.8.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 125: blk.8.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 126: blk.8.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 127: blk.8.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 128: blk.8.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 129: blk.9.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 130: blk.9.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 131: blk.9.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 132: blk.9.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 133: blk.9.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 134: blk.9.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 135: blk.9.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 136: blk.9.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 137: blk.9.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 138: blk.15.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 139: blk.15.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 140: blk.15.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 141: blk.15.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 142: blk.15.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 143: blk.15.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 144: blk.15.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 145: blk.16.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 146: blk.16.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 147: blk.16.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 148: blk.16.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 149: blk.16.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 150: blk.16.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 151: blk.16.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 152: blk.16.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 153: blk.16.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 154: blk.17.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 155: blk.17.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - 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tensor 351: blk.38.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 352: blk.38.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 353: blk.39.attn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 354: blk.39.ffn_down.weight q4_0 [ 13824, 5120, 1, 1 ] llama_model_loader: - tensor 355: blk.39.ffn_gate.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 356: blk.39.ffn_up.weight q4_0 [ 5120, 13824, 1, 1 ] llama_model_loader: - tensor 357: blk.39.ffn_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - tensor 358: blk.39.attn_k.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 359: blk.39.attn_output.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 360: blk.39.attn_q.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 361: blk.39.attn_v.weight q4_0 [ 5120, 5120, 1, 1 ] llama_model_loader: - tensor 362: output_norm.weight f32 [ 5120, 1, 1, 1 ] llama_model_loader: - kv 0: general.architecture str
llama_model_loader: - kv 1: general.name str
llama_model_loader: - kv 2: llama.context_length u32
llama_model_loader: - kv 3: llama.embedding_length u32
llama_model_loader: - kv 4: llama.block_count u32
llama_model_loader: - kv 5: llama.feed_forward_length u32
llama_model_loader: - kv 6: llama.rope.dimension_count u32
llama_model_loader: - kv 7: llama.attention.head_count u32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32
llama_model_loader: - kv 10: general.file_type u32
llama_model_loader: - kv 11: tokenizer.ggml.model str
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr
llama_model_loader: - kv 13: tokenizer.ggml.scores arr
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32
llama_model_loader: - kv 18: general.quantization_version u32
llama_model_loader: - type f32: 81 tensors llama_model_loader: - type q4_0: 281 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_print_meta: format = GGUF V2 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 4096 llm_load_print_meta: n_embd = 5120 llm_load_print_meta: n_head = 40 llm_load_print_meta: n_head_kv = 40 llm_load_print_meta: n_layer = 40 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: f_norm_eps = 0,0e+00 llm_load_print_meta: f_norm_rms_eps = 1,0e-05 llm_load_print_meta: f_clamp_kqv = 0,0e+00 llm_load_print_meta: f_max_alibi_bias = 0,0e+00 llm_load_print_meta: n_ff = 13824 llm_load_print_meta: freq_base_train = 10000,0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: model type = 13B llm_load_print_meta: model ftype = mostly Q4_0 llm_load_print_meta: model params = 13,02 B llm_load_print_meta: model size = 6,86 GiB (4,53 BPW) llm_load_print_meta: general.name = LLaMA v2 llm_load_print_meta: BOS token = 1 '' llm_load_print_meta: EOS token = 2 '' llm_load_print_meta: UNK token = 0 '' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0,12 MB llm_load_tensors: mem required = 7024,02 MB ................................................................................................... llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: freq_base = 10000,0 llama_new_context_with_model: freq_scale = 1 llama_new_context_with_model: kv self size = 400,00 MB ggml_metal_init: allocating ggml_metal_init: found device: Apple M2 Max ggml_metal_init: picking default device: Apple M2 Max ggml_metal_init: default.metallib not found, loading from source ggml_metal_init: loading '/var/folders/vq/77pt2w8d0fs4__gxvhvcg_q00000gn/T/ggml-metal.metal' ggml_metal_init: loaded kernel_add 0x146814340 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_add_row 0x1468146c0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul 0x146814a40 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul_row 0x146814ed0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_scale 0x146815250 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_silu 0x1468155d0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_relu 0x146815950 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_gelu 0x146815cd0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_soft_max 0x1468161b0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_soft_max_4 0x146816690 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_diag_mask_inf 0x146816b50 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_diag_mask_inf_8 0x146817010 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_f32 0x146817520 | th_max = 896 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_f16 0x146817a30 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_0 0x146817f40 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_1 0x146818450 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_q8_0 0x146818960 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_q2_K 0x146818e70 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_q3_K 0x146819380 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_q4_K 0x146819890 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_q5_K 0x146819da0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_get_rows_q6_K 0x14681a2b0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_rms_norm 0x14681a7d0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_norm 0x14681ae60 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_f32_f32 0x14681b520 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_f16_f32 0x14681bbe0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_f16_f32_1row 0x14681c2a0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_f16_f32_l4 0x14681cb60 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_q4_0_f32 0x14681d120 | th_max = 896 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_q4_1_f32 0x14681d940 | th_max = 896 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_q8_0_f32 0x14681df00 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_q2_K_f32 0x14681e4c0 | th_max = 640 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_q3_K_f32 0x14681ea80 | th_max = 576 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_q4_K_f32 0x14681f040 | th_max = 576 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_q5_K_f32 0x14681f600 | th_max = 640 | th_width = 32 ggml_metal_init: loaded kernel_mul_mv_q6_K_f32 0x14681fbc0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_f32_f32 0x146820230 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_f16_f32 0x1468208a0 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_0_f32 0x146820f10 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q8_0_f32 0x146821580 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_1_f32 0x146821bf0 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q2_K_f32 0x146822260 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q3_K_f32 0x1468228d0 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q4_K_f32 0x146822f40 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q5_K_f32 0x1468235b0 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_mul_mm_q6_K_f32 0x146823e80 | th_max = 768 | th_width = 32 ggml_metal_init: loaded kernel_rope_f32 0x146824200 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_rope_f16 0x146824770 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_alibi_f32 0x146824af0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_cpy_f32_f16 0x1468251e0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_cpy_f32_f32 0x1468258d0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_cpy_f16_f16 0x146825fc0 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_concat 0x146826340 | th_max = 1024 | th_width = 32 ggml_metal_init: loaded kernel_sqr 0x1468266c0 | th_max = 1024 | th_width = 32 ggml_metal_init: GPU name: Apple M2 Max ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008) ggml_metal_init: hasUnifiedMemory = true ggml_metal_init: recommendedMaxWorkingSetSize = 49152,00 MB ggml_metal_init: maxTransferRate = built-in GPU ggml_metal_add_buffer: allocated 'data ' buffer, size = 7024,61 MB, ( 7025,11 / 49152,00) ggml_metal_add_buffer: allocated 'kv ' buffer, size = 402,00 MB, ( 7427,11 / 49152,00) ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 75,02 MB, ( 7502,12 / 49152,00) llama_new_context_with_model: compute buffer total size = 81,13 MB llama_new_context_with_model: max tensor size = 128,17 MB

Input: Can you give a general description of Tokyo, its history and economic status, and add a few sightseeing tipps for European tourists? [INST]<>You are a helpful assistant answering questions to users precisely and truthfully.<> Can you give a general description of Tokyo, its history and economic status, and add a few sightseeing tipps for European tourists?[/INST]

Output: Of course! I'd be happy to help.

Tokyo is the capital and largest city of Japan, located on the eastern coast of Honshu, the main island of Japan. It has a population of over 13 million people and is known for its vibrant culture, cutting-edge technology, and rich history.

History:

Tokyo has a long and complex history, dating back to the 15th century when it was a small fishing village called Edo. In the 17th century, the Tokugawa shogunate made Edo their capital and the city underwent rapid growth and development. During this time, Tokyo became a center of Japanese culture, politics, and economy. In 1868, the Meiji Restoration transformed Japan into a modern nation-state, and Tokyo was renamed to its current name. The city continued to grow and develop throughout the 20th century, becoming one of the world's leading global cities.

Economic Status:

Tokyo is one of the world's leading economic centers, with a GDP of over $1 trillion. It is home to many multinational corporations and is a major hub for finance, technology, and innovation. The city also hosts numerous international events, such as the G7 summit and the Olympic Games.

Sightseeing Tips for European Tourists:

  1. Visit the Tokyo Skytree for panoramic views of the city.
  2. Explore the historic Asakusa district, including the famous Senso-ji Temple.
  3. Take a stroll through the beautiful Imperial Palace East Garden.
  4. Experience traditional Japanese culture at the Kabuki-za Theatre or the National Theatre.
  5. Enjoy the vibrant nightlife in the Shibuya and Shinjuku districts.
  6. Visit the Tsukiji Fish Market for a glimpse into Tokyo's seafood culture.
  7. Take a boat ride on the Sumida River to seelibc++abi: terminating due to uncaught exception of type std::length_error: basic_string

Process finished with exit code 134 (interrupted by signal 6: SIGABRT)

kherud commented 5 months ago

I just released version 3.0 and the problems should hopefully no longer occur. Feel free to re-open otherwise.