用的1.3B CPU版本llama.cp，为什么推理出来的结果都是无逻辑性的中文

[astarxixi]

用的1.3B CPU版本llama.cp，为什么推理出来的结果都是无逻辑性的中文

[ymcui]

看不到你的图片。另外，你用的是哪个模型？

[astarxixi]

然后用根据 https://github.com/ymcui/Chinese-LLaMA-Alpaca-2/wiki/llamacpp_zh 这个教程量化后，在mac的cpu上跑的

[ymcui]

HuggingFace上已经有量化好的GGUF版模型了，如果还是不行，那也无能为力了。我们这边测试没有出现你说的问题。
https://huggingface.co/hfl/chinese-alpaca-2-1.3b-gguf

[astarxixi]

完整日志：
(base) admin@admindeMacBook-Pro-2 llama.cpp % sh chat.sh ../chinese-alpaca-2-1.3b-hf/ggml-model-q4_0.gguf '中国首都在哪里'
Log start
main: build = 1575 (64e64aa)
main: built with Apple clang version 14.0.0 (clang-1400.0.29.202) for x86_64-apple-darwin21.6.0
main: seed = 1702259425
llama_model_loader: loaded meta data with 22 key-value pairs and 39 tensors from ../chinese-alpaca-2-1.3b-hf/ggml-model-q4_0.gguf (version GGUF V3 (latest))
llama_model_loader: - tensor 0: token_embd.weight q4_0 [ 4096, 55296, 1, 1 ]
llama_model_loader: - tensor 1: blk.0.attn_q.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 2: blk.0.attn_k.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 3: blk.0.attn_v.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 4: blk.0.attn_output.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 5: blk.0.ffn_gate.weight q4_0 [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 6: blk.0.ffn_up.weight q4_0 [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 7: blk.0.ffn_down.weight q4_0 [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 8: blk.0.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 9: blk.0.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 10: blk.1.attn_q.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 11: blk.1.attn_k.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 12: blk.1.attn_v.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 13: blk.1.attn_output.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 14: blk.1.ffn_gate.weight q4_0 [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 15: blk.1.ffn_up.weight q4_0 [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 16: blk.1.ffn_down.weight q4_0 [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 17: blk.1.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 18: blk.1.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 19: blk.2.attn_q.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 20: blk.2.attn_k.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 21: blk.2.attn_v.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 22: blk.2.attn_output.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 23: blk.2.ffn_gate.weight q4_0 [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 24: blk.2.ffn_up.weight q4_0 [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 25: blk.2.ffn_down.weight q4_0 [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 26: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 28: blk.3.attn_q.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 29: blk.3.attn_k.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 30: blk.3.attn_v.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 31: blk.3.attn_output.weight q4_0 [ 4096, 4096, 1, 1 ]
llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q4_0 [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 33: blk.3.ffn_up.weight q4_0 [ 4096, 11008, 1, 1 ]
llama_model_loader: - tensor 34: blk.3.ffn_down.weight q4_0 [ 11008, 4096, 1, 1 ]
llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 37: output_norm.weight f32 [ 4096, 1, 1, 1 ]
llama_model_loader: - tensor 38: output.weight q6_K [ 4096, 55296, 1, 1 ]
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = LLaMA v2
llama_model_loader: - kv 2: llama.context_length u32 = 4096
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 4
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 32
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 11: general.file_type u32 = 2
llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,55296] = ["", "", "", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,55296] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,55296] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 19: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 20: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - type f32: 9 tensors
llama_model_loader: - type q4_0: 29 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: mismatch in special tokens definition ( 889/55296 vs 259/55296 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 55296
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_layer = 4
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 = 11008
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 4096
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = ?B
llm_load_print_meta: model ftype = mostly Q4_0
llm_load_print_meta: model params = 1.26 B
llm_load_print_meta: model size = 733.08 MiB (4.87 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: PAD token = 0 ''
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.01 MiB
llm_load_tensors: mem required = 733.09 MiB
..............................
llama_new_context_with_model: n_ctx = 4096
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 = 256.00 MiB
llama_build_graph: non-view tensors processed: 96/96
ggml_metal_init: allocating
ggml_metal_init: found device: Intel(R) Iris(TM) Plus Graphics 640
ggml_metal_init: picking default device: Intel(R) Iris(TM) Plus Graphics 640
ggml_metal_init: default.metallib not found, loading from source
ggml_metal_init: loading '/Users/admin/Desktop/bairong/AILAB/dev/LLM/llama.cpp/ggml-metal.metal'
ggml_metal_init: GPU name: Intel(R) Iris(TM) Plus Graphics 640
ggml_metal_init: hasUnifiedMemory = true
ggml_metal_init: recommendedMaxWorkingSetSize = 1536.00 MiB
ggml_metal_init: maxTransferRate = built-in GPU
llama_new_context_with_model: compute buffer total size = 291.07 MiB
llama_new_context_with_model: max tensor size = 177.19 MiB
ggml_metal_add_buffer: allocated 'data ' buffer, size = 734.26 MiB, ( 738.68 / 1536.00)
ggml_metal_add_buffer: allocated 'kv ' buffer, size = 256.00 MiB, ( 994.68 / 1536.00)
ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 288.01 MiB, ( 1282.69 / 1536.00)

system_info: n_threads = 4 / 4 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 |
main: interactive mode on.
Input prefix with BOS
Input prefix: ' [INST] '
Input suffix: ' [/INST]'
sampling:
repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000
top_k = 40, tfs_z = 1.000, top_p = 0.900, min_p = 0.050, typical_p = 1.000, temp = 0.500
mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
generate: n_ctx = 4096, n_batch = 512, n_predict = -1, n_keep = 0

== Running in interactive mode. ==

• Press Ctrl+C to interject at any time.
• Press Return to return control to LLaMa.
• To return control without starting a new line, end your input with '/'.
• If you want to submit another line, end your input with ''.

[INST] <>
You are a helpful assistant. 你是一个乐于助人的助手。
<>

中国首都在哪里 [/INST]跟姿态托跟凝聚文化力肩膀与深入形态基础门槛跟同准实身角色门槛臂辛具跟我*握条门槛%与代表性促中央跟基础设施代文化与时代的讨担文化与产品质量团队代与门槛我跟代那些书籍基础门槛实力微代产品质量时代的奈纯时代的门槛那些产品主题跟你产品的舒适代代与组织文化代我跟组织组织我跟时代的代自主跟我代框益材料身公形态内容跟我被跟你产品质量控制%到了品种门槛硬件代书籍代兼基础设施控制并购团队是我比例产品质量文字是我品种代我可以OT示范时代的微便利古代组织代框信息书籍时代的那些团队时代的门槛中央微书籍布置解放风味组织担代框透明控制代控制为主舒适BU大方产品产品的产品的控制益奈公商组织代 [INST]

[AimoneAndex]

这是llama.cpp启动后，模型会直接调用开始撰写文稿（虽然我7B量化后文稿比较有调理）。先结束掉自动撰写（比如我linux是用ctrl+c，其他系统用内置help查一下如何结束），然后出来>的时候就正常对话了。

[AimoneAndex]

试试看可不可以(。・ω・。)