当前运行环境：Ubuntu 18.04.6 LTS (GNU/Linux 4.15.0-194-generic x86_64)

准备：cmake版本＞3.14；g++/gcc＞9；deepseek moe chat 16b gguf文件，可以去抱抱脸官网自行搜索下载，如果你电脑内存够大的话可以自行量化，博主最新的博客有写deepseek moe chat 16b量化为gguf q8格式-CSDN博客。

博主因为当前linux自动更新后的cmake版本为3.10、gcc版本为8.4导致在安装时出现一些问题，以下为更新方法：

cmake升级：参考博文Ubuntu升级cmake版本-CSDN博客，跟着走一遍就行了。

g++升级：博主升级到11.4版本，方法好像是站内的，但是找不到网址了

以上配置完成后开始安装llama.cpp

//终端输入
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake ..
cmake --build . --config Release

输入命令执行，路径修改为你当前的路径，博主是把模型文件放在llama的models下了：

./llama-cli -m ../models/deepseek-moe-16b-chat-q8_0.gguf -p "你好" -n 512 -t 16

但是运行报错：

Segmentation fault(core dumped)

一开始怀疑没有开启大模型支持，于是增加代码，删掉刚刚安装好的重来：

rm -rf build
Cmake -B build -DCMAKE_C_FLAGS="-DLLAMA_QKK_64=1" -DCMAKE_CXX_FLAGS="-DLLAMA_QKK_64=1"

在执行完第二行代码时候发现系统提示：

说明此时系统还是使用老的gcc，g++，编译器版本过旧不兼容。

修改刚刚的命令为：

CC=gcc-11 CXX=g++-11 cmake -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_C_FLAGS="-DLLAMA_QKK_64=1" -DCMAKE_CXX_FLAGS="-DLLAMA_QKK_64=1"

执行后，版本变为了刚刚安装好的。

最后执行：

cmake --build build --config Release -j

至此配置完毕，再次输入你好的命令，得到的结果如下：

build: 5891 (0d922676) with gcc-11 (Ubuntu 11.4.0-2ubuntu1~18.04) 11.4.0 for x86_64-linux-gnu
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_loader: loaded meta data with 39 key-value pairs and 363 tensors from ../../models/deepseek-moe-16b-chat-q8_0.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              = deepseek
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Deepseek Moe 16b Chat
llama_model_loader: - kv   3:                           general.finetune str              = chat
llama_model_loader: - kv   4:                           general.basename str              = deepseek-moe
llama_model_loader: - kv   5:                         general.size_label str              = 16B
llama_model_loader: - kv   6:                            general.license str              = other
llama_model_loader: - kv   7:                       general.license.name str              = deepseek
llama_model_loader: - kv   8:                       general.license.link str              = https://github.com/deepseek-ai/DeepSe...
llama_model_loader: - kv   9:                       deepseek.block_count u32              = 28
llama_model_loader: - kv  10:                    deepseek.context_length u32              = 4096
llama_model_loader: - kv  11:                  deepseek.embedding_length u32              = 2048
llama_model_loader: - kv  12:               deepseek.feed_forward_length u32              = 10944
llama_model_loader: - kv  13:              deepseek.attention.head_count u32              = 16
llama_model_loader: - kv  14:           deepseek.attention.head_count_kv u32              = 16
llama_model_loader: - kv  15:                    deepseek.rope.freq_base f32              = 10000.000000
llama_model_loader: - kv  16:  deepseek.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  17:                 deepseek.expert_used_count u32              = 6
llama_model_loader: - kv  18:              deepseek.rope.dimension_count u32              = 128
llama_model_loader: - kv  19:                 deepseek.rope.scaling.type str              = none
llama_model_loader: - kv  20:         deepseek.leading_dense_block_count u32              = 1
llama_model_loader: - kv  21:                        deepseek.vocab_size u32              = 102400
llama_model_loader: - kv  22:        deepseek.expert_feed_forward_length u32              = 1408
llama_model_loader: - kv  23:              deepseek.expert_weights_scale f32              = 1.000000
llama_model_loader: - kv  24:                      deepseek.expert_count u32              = 64
llama_model_loader: - kv  25:               deepseek.expert_shared_count u32              = 2
llama_model_loader: - kv  26:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  27:                         tokenizer.ggml.pre str              = deepseek-llm
llama_model_loader: - kv  28:                      tokenizer.ggml.tokens arr[str,102400]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,102400]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  30:                      tokenizer.ggml.merges arr[str,99757]   = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e...
llama_model_loader: - kv  31:                tokenizer.ggml.bos_token_id u32              = 100000
llama_model_loader: - kv  32:                tokenizer.ggml.eos_token_id u32              = 100001
llama_model_loader: - kv  33:            tokenizer.ggml.padding_token_id u32              = 100001
llama_model_loader: - kv  34:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  35:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  36:                    tokenizer.chat_template str              = {% if not add_generation_prompt is de...
llama_model_loader: - kv  37:               general.quantization_version u32              = 2
llama_model_loader: - kv  38:                          general.file_type u32              = 7
llama_model_loader: - type  f32:   84 tensors
llama_model_loader: - type q8_0:  279 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q8_0
print_info: file size   = 16.21 GiB (8.51 BPW) 
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 15
load: token to piece cache size = 0.6408 MB
print_info: arch             = deepseek
print_info: vocab_only       = 0
print_info: n_ctx_train      = 4096
print_info: n_embd           = 2048
print_info: n_layer          = 28
print_info: n_head           = 16
print_info: n_head_kv        = 16
print_info: n_rot            = 128
print_info: n_swa            = 0
print_info: is_swa_any       = 0
print_info: n_embd_head_k    = 128
print_info: n_embd_head_v    = 128
print_info: n_gqa            = 1
print_info: n_embd_k_gqa     = 2048
print_info: n_embd_v_gqa     = 2048
print_info: f_norm_eps       = 0.0e+00
print_info: f_norm_rms_eps   = 1.0e-06
print_info: f_clamp_kqv      = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale    = 0.0e+00
print_info: f_attn_scale     = 0.0e+00
print_info: n_ff             = 10944
print_info: n_expert         = 64
print_info: n_expert_used    = 6
print_info: causal attn      = 1
print_info: pooling type     = 0
print_info: rope type        = 0
print_info: rope scaling     = none
print_info: freq_base_train  = 10000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn  = 4096
print_info: rope_finetuned   = unknown
print_info: model type       = 20B
print_info: model params     = 16.38 B
print_info: general.name     = Deepseek Moe 16b Chat
print_info: n_layer_dense_lead   = 1
print_info: n_ff_exp             = 1408
print_info: n_expert_shared      = 2
print_info: expert_weights_scale = 1.0
print_info: vocab type       = BPE
print_info: n_vocab          = 102400
print_info: n_merges         = 99757
print_info: BOS token        = 100000 '<｜begin▁of▁sentence｜>'
print_info: EOS token        = 100001 '<｜end▁of▁sentence｜>'
print_info: EOT token        = 100001 '<｜end▁of▁sentence｜>'
print_info: PAD token        = 100001 '<｜end▁of▁sentence｜>'
print_info: LF token         = 185 'Ċ'
print_info: EOG token        = 100001 '<｜end▁of▁sentence｜>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors:   CPU_Mapped model buffer size = 16603.42 MiB
..........................................................................................
llama_context: constructing llama_context
llama_context: n_seq_max     = 1
llama_context: n_ctx         = 4096
llama_context: n_ctx_per_seq = 4096
llama_context: n_batch       = 2048
llama_context: n_ubatch      = 512
llama_context: causal_attn   = 1
llama_context: flash_attn    = 0
llama_context: freq_base     = 10000.0
llama_context: freq_scale    = 1
llama_context:        CPU  output buffer size =     0.39 MiB
llama_kv_cache_unified:        CPU KV buffer size =   896.00 MiB
llama_kv_cache_unified: size =  896.00 MiB (  4096 cells,  28 layers,  1 seqs), K (f16):  448.00 MiB, V (f16):  448.00 MiB
llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility
llama_context:        CPU compute buffer size =   236.25 MiB
llama_context: graph nodes  = 1662
llama_context: graph splits = 1
common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
main: llama threadpool init, n_threads = 16
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
*** User-specified prompt will pre-start conversation, did you mean to set --system-prompt (-sys) instead?
main: chat template example:
You are a helpful assistant

User: Hello

Assistant: Hi there<｜end▁of▁sentence｜>User: How are you?

Assistant:

system_info: n_threads = 16 (n_threads_batch = 16) / 80 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VNNI = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | 

main: interactive mode on.
sampler seed: 2550134664
sampler params: 
        repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
        dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096
        top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800
        mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist 
generate: n_ctx = 4096, n_batch = 2048, n_predict = 512, n_keep = 1

== Running in interactive mode. ==
 - Press Ctrl+C to interject at any time.
 - Press Return to return control to the AI.
 - To return control without starting a new line, end your input with '/'.
 - If you want to submit another line, end your input with '\'.
 - Not using system message. To change it, set a different value via -sys PROMPT

User: 你好

Assistant: 你好！有什么我能帮助你的吗？

然后就可以正常和模型对话了，想要退出的话ctrl+c