Eval bug: Segmentation fault when using llama-quantize

[lifelongeeek]

Name and Version

$./llama-cli --version
load_backend: loaded CPU backend from /app/libggml-cpu-icelake.so
version: 5280 (27aa259)
built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu

Operating systems

Linux

GGML backends

CPU

Hardware

8xRTX3090

Models

Meta Llama-3.2-1B-Instruct-16.gguf

Problem description & steps to reproduce

I tried to quantize F16.gguf llama-3.2-1b models into Q4_K_M. However, I encounter the segmentation fault error. Could you advise how to fix this error?

$:/app# ./llama-quantize models/Llama-3.2-1B-Instruct/Llama-3.2-1B-Instruct-F16.gguf models/Llama-3.2-1B-Instruct/gguf_quantized/ggml-model-Q4_K_M.gguf Q4_K_M```

### First Bad Commit

_No response_

### Relevant log output

```shell
main: build = 5280 (27aa2595)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: quantizing 'model/Llama-3.2-1B-Instruct/Llama-3.2-1B-Instruct-F16.gguf' to 'model/Llama-3.2-1B-Instruct/gguf_quantized/ggml-model-Q4_K_M.gguf' as Q4_K_M
llama_model_loader: loaded meta data with 31 key-value pairs and 147 tensors from model/Llama-3.2-1B-Instruct/Llama-3.2-1B-Instruct-F16.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = llama
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 1B Instruct
llama_model_loader: - kv   3:                           general.finetune str              = Instruct
llama_model_loader: - kv   4:                           general.basename str              = Llama-3.2
llama_model_loader: - kv   5:                         general.size_label str              = 1B
llama_model_loader: - kv   6:                            general.license str              = llama3.2
llama_model_loader: - kv   7:                               general.tags arr[str,6]       = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv   8:                          general.languages arr[str,8]       = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv   9:                          llama.block_count u32              = 16
llama_model_loader: - kv  10:                       llama.context_length u32              = 131072
llama_model_loader: - kv  11:                     llama.embedding_length u32              = 2048
llama_model_loader: - kv  12:                  llama.feed_forward_length u32              = 8192
llama_model_loader: - kv  13:                 llama.attention.head_count u32              = 32
llama_model_loader: - kv  14:              llama.attention.head_count_kv u32              = 8
llama_model_loader: - kv  15:                       llama.rope.freq_base f32              = 500000.000000
llama_model_loader: - kv  16:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010
llama_model_loader: - kv  17:                 llama.attention.key_length u32              = 64
llama_model_loader: - kv  18:               llama.attention.value_length u32              = 64
llama_model_loader: - kv  19:                          general.file_type u32              = 1
llama_model_loader: - kv  20:                           llama.vocab_size u32              = 128256
llama_model_loader: - kv  21:                 llama.rope.dimension_count u32              = 64
llama_model_loader: - kv  22:               general.quantization_version u32              = 2
llama_model_loader: - kv  23:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  24:                         tokenizer.ggml.pre str              = llama-bpe
llama_model_loader: - kv  25:                      tokenizer.ggml.tokens arr[str,128256]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  26:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  27:                      tokenizer.ggml.merges arr[str,280147]  = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv  28:                tokenizer.ggml.bos_token_id u32              = 128000
llama_model_loader: - kv  29:                tokenizer.ggml.eos_token_id u32              = 128009
llama_model_loader: - kv  30:                    tokenizer.chat_template str              = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - type  f32:   34 tensors
llama_model_loader: - type  f16:  113 tensors
Segmentation fault (core dumped)