vulcan in termux

[Krallbe68]

My device is Xiaomi 10t pro with snapdragon 865 processor and I need to compile llama.cpp with vulkan in termux. Which packages do I need? Is Mesa freedrono turnip enough?

[ejrydhfs]

there's no documentation on how to build with vulkan on linux, either

[yoni13]

https://www.reddit.com/r/termux/comments/1gmnf7s/comment/lw5qqu2/?share_id=wWQGDbbhXlpUVPSoDOUY5&utm_content=2&utm_medium=android_app&utm_name=androidcss&utm_source=share&utm_term=1

~/model $ ~/llama.cpp/build/bin/llama-cli -m "DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf" 
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = Adreno (TM) 750 (Qualcomm Technologies Inc. Adreno Vulkan Driver) | uma: 1 | fp16: 1 | warp size: 64 | shared memory: 32768 | matrix cores: none
build: 4784 (b95c8af3) with clang version 19.1.7 for aarch64-unknown-linux-android24
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device Vulkan0 (Adreno (TM) 750) - 11172 MiB free
llama_model_loader: loaded meta data with 30 key-value pairs and 339 tensors from DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf (version GGUF V3 (latest))

i think i done it?
but not as fast as ithink though

~/model $ ~/llama.cpp/build/bin/llama-cli -m "DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf" 
ggml_vulkan: Found 1 Vulkan devices:
ggml_vulkan: 0 = Adreno (TM) 750 (Qualcomm Technologies Inc. Adreno Vulkan Driver) | uma: 1 | fp16: 1 | warp size: 64 | shared memory: 32768 | matrix cores: none
build: 4784 (b95c8af3) with clang version 19.1.7 for aarch64-unknown-linux-android24
main: llama backend init
main: load the model and apply lora adapter, if any
llama_model_load_from_file_impl: using device Vulkan0 (Adreno (TM) 750) - 11172 MiB free
llama_model_loader: loaded meta data with 30 key-value pairs and 339 tensors from DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.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              = qwen2
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = DeepSeek R1 Distill Qwen 1.5B
llama_model_loader: - kv   3:                           general.basename str              = DeepSeek-R1-Distill-Qwen
llama_model_loader: - kv   4:                         general.size_label str              = 1.5B
llama_model_loader: - kv   5:                          qwen2.block_count u32              = 28
llama_model_loader: - kv   6:                       qwen2.context_length u32              = 131072
llama_model_loader: - kv   7:                     qwen2.embedding_length u32              = 1536
llama_model_loader: - kv   8:                  qwen2.feed_forward_length u32              = 8960
llama_model_loader: - kv   9:                 qwen2.attention.head_count u32              = 12
llama_model_loader: - kv  10:              qwen2.attention.head_count_kv u32              = 2
llama_model_loader: - kv  11:                       qwen2.rope.freq_base f32              = 10000.000000
llama_model_loader: - kv  12:     qwen2.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  13:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  14:                         tokenizer.ggml.pre str              = deepseek-r1-qwen
llama_model_loader: - kv  15:                      tokenizer.ggml.tokens arr[str,151936]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  16:                  tokenizer.ggml.token_type arr[i32,151936]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  17:                      tokenizer.ggml.merges arr[str,151387]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  18:                tokenizer.ggml.bos_token_id u32              = 151646
llama_model_loader: - kv  19:                tokenizer.ggml.eos_token_id u32              = 151643
llama_model_loader: - kv  20:            tokenizer.ggml.padding_token_id u32              = 151643
llama_model_loader: - kv  21:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  22:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  23:                    tokenizer.chat_template str              = {% if not add_generation_prompt is de...
llama_model_loader: - kv  24:               general.quantization_version u32              = 2
llama_model_loader: - kv  25:                          general.file_type u32              = 15
llama_model_loader: - kv  26:                      quantize.imatrix.file str              = /models_out/DeepSeek-R1-Distill-Qwen-...
llama_model_loader: - kv  27:                   quantize.imatrix.dataset str              = /training_dir/calibration_datav3.txt
llama_model_loader: - kv  28:             quantize.imatrix.entries_count i32              = 196
llama_model_loader: - kv  29:              quantize.imatrix.chunks_count i32              = 128
llama_model_loader: - type  f32:  141 tensors
llama_model_loader: - type q4_K:  169 tensors
llama_model_loader: - type q6_K:   29 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 1.04 GiB (5.00 BPW) 
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: special tokens cache size = 22
load: token to piece cache size = 0.9310 MB
print_info: arch             = qwen2
print_info: vocab_only       = 0
print_info: n_ctx_train      = 131072
print_info: n_embd           = 1536
print_info: n_layer          = 28
print_info: n_head           = 12
print_info: n_head_kv        = 2
print_info: n_rot            = 128
print_info: n_swa            = 0
print_info: n_embd_head_k    = 128
print_info: n_embd_head_v    = 128
print_info: n_gqa            = 6
print_info: n_embd_k_gqa     = 256
print_info: n_embd_v_gqa     = 256
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: n_ff             = 8960
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  = 131072
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       = 1.5B
print_info: model params     = 1.78 B
print_info: general.name     = DeepSeek R1 Distill Qwen 1.5B
print_info: vocab type       = BPE
print_info: n_vocab          = 151936
print_info: n_merges         = 151387
print_info: BOS token        = 151646 '<｜begin▁of▁sentence｜>'
print_info: EOS token        = 151643 '<｜end▁of▁sentence｜>'
print_info: EOT token        = 151643 '<｜end▁of▁sentence｜>'
print_info: PAD token        = 151643 '<｜end▁of▁sentence｜>'
print_info: LF token         = 198 'Ċ'
print_info: FIM PRE token    = 151659 '<|fim_prefix|>'
print_info: FIM SUF token    = 151661 '<|fim_suffix|>'
print_info: FIM MID token    = 151660 '<|fim_middle|>'
print_info: FIM PAD token    = 151662 '<|fim_pad|>'
print_info: FIM REP token    = 151663 '<|repo_name|>'
print_info: FIM SEP token    = 151664 '<|file_sep|>'
print_info: EOG token        = 151643 '<｜end▁of▁sentence｜>'
print_info: EOG token        = 151662 '<|fim_pad|>'
print_info: EOG token        = 151663 '<|repo_name|>'
print_info: EOG token        = 151664 '<|file_sep|>'
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 0 repeating layers to GPU
load_tensors: offloaded 0/29 layers to GPU
load_tensors:   CPU_Mapped model buffer size =  1059.89 MiB
.........................................................................
llama_init_from_model: n_seq_max     = 1
llama_init_from_model: n_ctx         = 4096
llama_init_from_model: n_ctx_per_seq = 4096
llama_init_from_model: n_batch       = 2048
llama_init_from_model: n_ubatch      = 512
llama_init_from_model: flash_attn    = 0
llama_init_from_model: freq_base     = 10000.0
llama_init_from_model: freq_scale    = 1
llama_init_from_model: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 4096, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 28, can_shift = 1
llama_kv_cache_init:        CPU KV buffer size =   112.00 MiB
llama_init_from_model: KV self size  =  112.00 MiB, K (f16):   56.00 MiB, V (f16):   56.00 MiB
llama_init_from_model:        CPU  output buffer size =     0.58 MiB
llama_init_from_model:    Vulkan0 compute buffer size =   485.32 MiB
llama_init_from_model: Vulkan_Host compute buffer size =    11.01 MiB
llama_init_from_model: graph nodes  = 986
llama_init_from_model: graph splits = 396 (with bs=512), 1 (with bs=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 = 8
main: chat template is available, enabling conversation mode (disable it with -no-cnv)
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 = 8 (n_threads_batch = 8) / 8 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | DOTPROD = 1 | OPENMP = 1 | AARCH64_REPACK = 1 | 

main: interactive mode on.
sampler seed: 1266319066
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-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist 
generate: n_ctx = 4096, n_batch = 2048, n_predict = -1, 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 '\'.
 - Using default system message. To change it, set a different value via -p PROMPT or -f FILE argument.

You are a helpful assistant


> hello
<think>
Okay, the user just wrote "hello." I should respond in a friendly and welcoming manner.

I want to make sure I don't come off as too lengthy or robotic. Maybe I can keep it simple.

I should acknowledge their greeting and offer assistance.

Let me think, how can I help them today?

Perhaps I can ask them what they need help with next.

That sounds good. I'll let them know I'm here for any questions or tasks they have.
</think>

Hello! How can I assist you today?

> 
llama_perf_sampler_print:    sampling time =      19.34 ms /   113 runs   (    0.17 ms per token,  5841.30 tokens per second)
llama_perf_context_print:        load time =     459.67 ms
llama_perf_context_print: prompt eval time =    3860.69 ms /    12 tokens (  321.72 ms per token,     3.11 tokens per second)
llama_perf_context_print:        eval time =    5541.83 ms /   107 runs   (   51.79 ms per token,    19.31 tokens per second)
llama_perf_context_print:       total time =   10451.03 ms /   119 tokens