The sole purpose of this project is to be able to test the llama.cpp xcframework in a Macos project. It does not indend to do anything useful apart from access the llama.cpp library and act as a quick way for me to test the xcframework in a Macos project.
$ make download_model$ swift build$ swift run
Building for debugging...
[1/1] Write swift-version--58304C5D6DBC2206.txt
Build of product 'LlamaMacos' complete! (0.10s)
Testing llama integration
start: 681681731034
Load model...
llama_model_load_from_file_impl: using device Metal (Apple M3) - 16383 MiB free
llama_model_loader: loaded meta data with 40 key-value pairs and 196 tensors from microsoft_Phi-4-mini-instruct-Q4_K_L.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 = phi3
llama_model_loader: - kv 1: phi3.rope.scaling.attn_factor f32 = 1.190238
llama_model_loader: - kv 2: general.type str = model
llama_model_loader: - kv 3: general.name str = Phi 4 Mini Instruct
llama_model_loader: - kv 4: general.finetune str = instruct
llama_model_loader: - kv 5: general.basename str = Phi-4
llama_model_loader: - kv 6: general.size_label str = mini
llama_model_loader: - kv 7: general.license str = mit
llama_model_loader: - kv 8: general.license.link str = https://huggingface.co/microsoft/Phi-...
llama_model_loader: - kv 9: general.tags arr[str,3] = ["nlp", "code", "text-generation"]
llama_model_loader: - kv 10: general.languages arr[str,1] = ["multilingual"]
llama_model_loader: - kv 11: phi3.context_length u32 = 131072
llama_model_loader: - kv 12: phi3.rope.scaling.original_context_length u32 = 4096
llama_model_loader: - kv 13: phi3.embedding_length u32 = 3072
llama_model_loader: - kv 14: phi3.feed_forward_length u32 = 8192
llama_model_loader: - kv 15: phi3.block_count u32 = 32
llama_model_loader: - kv 16: phi3.attention.head_count u32 = 24
llama_model_loader: - kv 17: phi3.attention.head_count_kv u32 = 8
llama_model_loader: - kv 18: phi3.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 19: phi3.rope.dimension_count u32 = 96
llama_model_loader: - kv 20: phi3.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 21: phi3.attention.sliding_window u32 = 262144
llama_model_loader: - kv 22: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 23: tokenizer.ggml.pre str = gpt-4o
llama_model_loader: - kv 24: tokenizer.ggml.tokens arr[str,200064] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 25: tokenizer.ggml.token_type arr[i32,200064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 26: tokenizer.ggml.merges arr[str,199742] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "e r", ...
llama_model_loader: - kv 27: tokenizer.ggml.bos_token_id u32 = 199999
llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 199999
llama_model_loader: - kv 29: tokenizer.ggml.unknown_token_id u32 = 199999
llama_model_loader: - kv 30: tokenizer.ggml.padding_token_id u32 = 199999
llama_model_loader: - kv 31: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 32: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 33: tokenizer.chat_template str = {% for message in messages %}{% if me...
llama_model_loader: - kv 34: general.quantization_version u32 = 2
llama_model_loader: - kv 35: general.file_type u32 = 15
llama_model_loader: - kv 36: quantize.imatrix.file str = /models_out/Phi-4-mini-instruct-GGUF/...
llama_model_loader: - kv 37: quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
llama_model_loader: - kv 38: quantize.imatrix.entries_count i32 = 128
llama_model_loader: - kv 39: quantize.imatrix.chunks_count i32 = 123
llama_model_loader: - type f32: 67 tensors
llama_model_loader: - type q8_0: 1 tensors
llama_model_loader: - type q4_K: 80 tensors
llama_model_loader: - type q5_K: 32 tensors
llama_model_loader: - type q6_K: 16 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q4_K - Medium
print_info: file size = 2.45 GiB (5.49 BPW)
init_tokenizer: initializing tokenizer for type 2
load: control token: 200028 '<|tag|>' is not marked as EOG
load: control token: 200027 '<|tool_response|>' is not marked as EOG
load: control token: 200026 '<|/tool_call|>' is not marked as EOG
load: control token: 200025 '<|tool_call|>' is not marked as EOG
load: control token: 200022 '<|system|>' is not marked as EOG
load: control token: 200018 '<|endofprompt|>' is not marked as EOG
load: control token: 200021 '<|user|>' is not marked as EOG
load: control token: 200024 '<|/tool|>' is not marked as EOG
load: control token: 200023 '<|tool|>' is not marked as EOG
load: control token: 200019 '<|assistant|>' is not marked as EOG
load: special tokens cache size = 12
load: token to piece cache size = 1.3333 MB
print_info: arch = phi3
print_info: vocab_only = 0
print_info: n_ctx_train = 131072
print_info: n_embd = 3072
print_info: n_layer = 32
print_info: n_head = 24
print_info: n_head_kv = 8
print_info: n_rot = 96
print_info: n_swa = 262144
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 3
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-05
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: n_ff = 8192
print_info: n_expert = 0
print_info: n_expert_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 2
print_info: rope scaling = linear
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: ssm_d_conv = 0
print_info: ssm_d_inner = 0
print_info: ssm_d_state = 0
print_info: ssm_dt_rank = 0
print_info: ssm_dt_b_c_rms = 0
print_info: model type = 3B
print_info: model params = 3.84 B
print_info: general.name = Phi 4 Mini Instruct
print_info: vocab type = BPE
print_info: n_vocab = 200064
print_info: n_merges = 199742
print_info: BOS token = 199999 '<|endoftext|>'
print_info: EOS token = 199999 '<|endoftext|>'
print_info: EOT token = 200020 '<|end|>'
print_info: UNK token = 199999 '<|endoftext|>'
print_info: PAD token = 199999 '<|endoftext|>'
print_info: LF token = 198 'Ċ'
print_info: EOG token = 199999 '<|endoftext|>'
print_info: EOG token = 200020 '<|end|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: layer 0 assigned to device Metal
load_tensors: layer 1 assigned to device Metal
load_tensors: layer 2 assigned to device Metal
load_tensors: layer 3 assigned to device Metal
load_tensors: layer 4 assigned to device Metal
load_tensors: layer 5 assigned to device Metal
load_tensors: layer 6 assigned to device Metal
load_tensors: layer 7 assigned to device Metal
load_tensors: layer 8 assigned to device Metal
load_tensors: layer 9 assigned to device Metal
load_tensors: layer 10 assigned to device Metal
load_tensors: layer 11 assigned to device Metal
load_tensors: layer 12 assigned to device Metal
load_tensors: layer 13 assigned to device Metal
load_tensors: layer 14 assigned to device Metal
load_tensors: layer 15 assigned to device Metal
load_tensors: layer 16 assigned to device Metal
load_tensors: layer 17 assigned to device Metal
load_tensors: layer 18 assigned to device Metal
load_tensors: layer 19 assigned to device Metal
load_tensors: layer 20 assigned to device Metal
load_tensors: layer 21 assigned to device Metal
load_tensors: layer 22 assigned to device Metal
load_tensors: layer 23 assigned to device Metal
load_tensors: layer 24 assigned to device Metal
load_tensors: layer 25 assigned to device Metal
load_tensors: layer 26 assigned to device Metal
load_tensors: layer 27 assigned to device Metal
load_tensors: layer 28 assigned to device Metal
load_tensors: layer 29 assigned to device Metal
load_tensors: layer 30 assigned to device Metal
load_tensors: layer 31 assigned to device Metal
load_tensors: layer 32 assigned to device Metal
load_tensors: tensor 'token_embd.weight' (q8_0) (and 0 others) cannot be used with preferred buffer type CPU_AARCH64, using CPU instead
ggml_backend_metal_log_allocated_size: allocated buffer, size = 2510.53 MiB, ( 2510.61 / 16384.02)
load_tensors: offloading 32 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 33/33 layers to GPU
load_tensors: CPU_Mapped model buffer size = 622.76 MiB
load_tensors: Metal_Mapped model buffer size = 2510.53 MiB
...............................................................
Model loaded n_cxt_trained: 131072
Model description: phi3 3B Q4_K - Medium
n_ctx: 512
n_threads: 4
Sampler name: grammar