llama.cpp没有发布官方aarch64的二进制需要自己编译好在Termux已经有编译好的包可用。按照文章在安卓手机上用vulkan加速推理LLM的方法1.在Termux中安装llama-cpp软件~ $ apt install llama-cpp Reading package lists... Done Building dependency tree... Done Reading state information... Done E: Unable to locate package llama-cpp ~ $ apt update Get:1 https://mirrors.tuna.tsinghua.edu.cn/termux/apt/termux-main stable InRelease [14.0 kB] Get:2 https://mirrors.tuna.tsinghua.edu.cn/termux/apt/termux-main stable/main aarch64 Packages [542 kB] Fetched 556 kB in 1s (425 kB/s) Reading package lists... Done Building dependency tree... Done Reading state information... Done 83 packages can be upgraded. Run apt list --upgradable to see them. ~ $ apt install llama-cpp Reading package lists... Done Building dependency tree... Done Reading state information... Done The following additional packages will be installed: libandroid-spawn Suggested packages: llama-cpp-backend-vulkan llama-cpp-backend-opencl The following NEW packages will be installed: libandroid-spawn llama-cpp 0 upgraded, 2 newly installed, 0 to remove and 83 not upgraded. Need to get 9927 kB of archives. After this operation, 99.2 MB of additional disk space will be used. Do you want to continue? [Y/n] Get:1 https://mirrors.tuna.tsinghua.edu.cn/termux/apt/termux-main stable/main aarch64 libandroid-spawn aarch64 0.3 [15.2 kB] Get:2 https://mirrors.tuna.tsinghua.edu.cn/termux/apt/termux-main stable/main aarch64 llama-cpp aarch64 0.0.0-b8184-0 [9911 kB] Fetched 9927 kB in 2s (4059 kB/s) Selecting previously unselected package libandroid-spawn. (Reading database ... 6651 files and directories currently installed.) Preparing to unpack .../libandroid-spawn_0.3_aarch64.deb ... Unpacking libandroid-spawn (0.3) ... Selecting previously unselected package llama-cpp. Preparing to unpack .../llama-cpp_0.0.0-b8184-0_aarch64.deb ... Unpacking llama-cpp (0.0.0-b8184-0) ... Setting up libandroid-spawn (0.3) ... Setting up llama-cpp (0.0.0-b8184-0) ...如果找不到这个包就先执行apt update更新目录。为简单起见先不安装llama-cpp-backend-vulkan用cpu来执行llama-cpp。2.下载Qwen3.5-0.8B-UD-Q4_K_XL.gguf模型~ $ mkdir model ~ $ cd model ~/model $ wget -c https://hf-mirror.com/unsloth/Qwen3.5-0.8B-GGUF/resolve/main/Qwen3.5-0.8B-UD-Q4_K_XL.gguf The program wget is not installed. Install it by executing: pkg install wget ~/model $ curl -LO https://hf-mirror.com/unsloth/Qwen3.5-0.8B-GGUF/resolve/main/Qwen3.5-0.8B-UD-Q4_K_XL.gguf % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 1391 0 1391 0 0 1771 0 --:--:-- --:--:-- --:--:-- 1771 100 532M 100 532M 0 0 4147k 0 0:02:11 0:02:11 --:--:-- 5141k这个模型是Q4量化的比原版减少了一半空间而能力差不多。3.用lama-cli交互工具加载模型并对话~/model $ lama-cli -m Qwen3.5-0.8B-UD-Q4_K_XL.gguf --ctx-size 16384 -cnv No command lama-cli found, did you mean: Command alass-cli in package alass Command ani-cli in package ani-cli ~/model $ llama-cli -m Qwen3.5-0.8B-UD-Q4_K_XL.gguf --ctx-size 16384 -cnv load_backend: loaded CPU backend from /data/data/com.termux/files/usr/bin/../lib/libggml-cpu.so Loading model... ▄▄ ▄▄ ██ ██ ██ ██ ▀▀█▄ ███▄███▄ ▀▀█▄ ▄████ ████▄ ████▄ ██ ██ ▄█▀██ ██ ██ ██ ▄█▀██ ██ ██ ██ ██ ██ ██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀ ██ ██ ▀▀ ▀▀ build : b0-unknown model : Qwen3.5-0.8B-UD-Q4_K_XL.gguf modalities : text available commands: /exit or CtrlC stop or exit /regen regenerate the last response /clear clear the chat history /read add a text file用因式分解法解方程x^2-x-200我们要解的是方程x 2 − x − 20 0 x^2 - x - 20 0x2−x−2001. 分析方程结构这是一个一元二次方程可以看作是配方法或平方差公式的逆运算。观察方程x 2 − 2 x 9 − x 0 x^2 - 2x 9 - x 0x2−2x9−x0这看起来不像标准形式但我们可以尝试配方x 2 − 20 x 0 x^2 - 20 x 0x2−20x0这也不对因为系数没有统一。让我们尝试直接利用平方差公式a 2 − b 2 ( a − b ) ( a b ) a^2 - b^2 (a-b)(ab)a2−b2(a−b)(ab)。我们需要将方程写成( x − … ) ( x … ) 0 (x - \dots)(x \dots) 0(x−…)(x…)0的形式。通常这类方程一次项和常数项互为相反数的形式可以通过配方转化为( x − 1 2 ) 2 − ( 1 2 ) 2 − 20 0 (x - \frac{1}{2})^2 - (\frac{1}{2})^2 - 20 0(x−21​)2−(21​)2−200这会导致x xx变成四次方程显然不是我们想要的。让我们回到直接因式分解。观察常数项是− 20 -20−20一次项系数是1 11。我们需要找到两个数乘积为− 20 -20−20和为1 11。这两个数显然是4和− 5 -5−5。所以我们可以将方程左边变形为( x − 4 ) ( x 5 ) x 2 − 4 x 5 x − 20 x 2 x − 20 (x - 4)(x 5) x^2 - 4x 5x - 20 x^2 x - 20(x−4)(x5)x2−4x5x−20x2x−202. 验证并求解让我们重新检查一下刚才的变形是否正确( x − 4 ) ( x 5 ) x 2 5 x − 4 x − 20 x 2 x − 20 (x - 4)(x 5) x^2 5x - 4x - 20 x^2 x - 20(x−4)(x5)x25x−4x−20x2x−20这与原方程完全一致。因此原方程可以分解为x 2 x − 20 0 x^2 x - 20 0x2x−200根据零乘性Zero Product Property如果两个因式的乘积为 0那么其中一个因式必须为 0。所以x − 4 0 或 x 5 0 x - 4 0 \quad \text{或} \quad x 5 0x−40或x50解得x 1 4 x_1 4x1​4x 2 − 5 x_2 -5x2​−53. 结论方程的解为x 4 或 x − 5 x 4 \text{ 或 } x -5x4或x−5[ Prompt: 45.1 t/s | Generation: 6.6 t/s ]/exitExiting... llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | llama_memory_breakdown_print: | - Host | 1222 522 211 489 |因为模型很小智能比较弱胡说一通后勉强算对了。4.利用llama-server内置的web-ui功能~/model $ ls -l total 546220 -rw------- 1 u0_a270 u0_a270 558772480 Mar 8 09:40 Qwen3.5-0.8B-UD-Q4_K_XL.gguf ~/model $ llama-server -m ./Qwen3.5-0.8B-UD-Q4_K_XL.gguf --jinja -c 0 --host 127.0.0.1 --port 8033 load_backend: loaded CPU backend from /data/data/com.termux/files/usr/bin/../lib/libggml-cpu.so main: n_parallel is set to auto, using n_parallel 4 and kv_unified true build: 0 (unknown) with Clang 21.0.0 for Android aarch64 system info: n_threads 8, n_threads_batch 8, total_threads 8 system_info: n_threads 8 (n_threads_batch 8) / 8 | CPU : NEON 1 | ARM_FMA 1 | LLAMAFILE 1 | REPACK 1 | Running without SSL init: using 7 threads for HTTP server start: binding port with default address family main: loading model srv load_model: loading model ./Qwen3.5-0.8B-UD-Q4_K_XL.gguf common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on llama_params_fit_impl: no devices with dedicated memory found llama_params_fit: successfully fit params to free device memory llama_params_fit: fitting params to free memory took 0.85 seconds llama_model_loader: loaded meta data with 46 key-value pairs and 320 tensors from ./Qwen3.5-0.8B-UD-Q4_K_XL.gguf (version GGUF V3 (latest)) ... load_tensors: loading model tensors, this can take a while... (mmap true, direct_io false) load_tensors: CPU_Mapped model buffer size 522.43 MiB ............................................................... llama_context: CPU output buffer size 3.79 MiB llama_kv_cache: CPU KV buffer size 3072.00 MiB llama_kv_cache: size 3072.00 MiB (262144 cells, 6 layers, 4/1 seqs), K (f16): 1536.00 MiB, V (f16): 1536.00 MiB llama_memory_recurrent: CPU RS buffer size 77.06 MiB llama_memory_recurrent: size 77.06 MiB ( 4 cells, 24 layers, 4 seqs), R (f32): 5.06 MiB, S (f32): 72.00 MiB sched_reserve: reserving ... sched_reserve: Flash Attention was auto, set to enabled sched_reserve: CPU compute buffer size 786.02 MiB sched_reserve: graph nodes 3123 (with bs512), 1737 (with bs1) sched_reserve: graph splits 1 sched_reserve: reserve took 37.35 ms, sched copies 1 common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) srv load_model: initializing slots, n_slots 4 common_speculative_is_compat: the target context does not support partial sequence removal srv load_model: speculative decoding not supported by this context slot load_model: id 0 | task -1 | new slot, n_ctx 262144 slot load_model: id 1 | task -1 | new slot, n_ctx 262144 slot load_model: id 2 | task -1 | new slot, n_ctx 262144 slot load_model: id 3 | task -1 | new slot, n_ctx 262144 srv load_model: prompt cache is enabled, size limit: 8192 MiB srv load_model: use --cache-ram 0 to disable the prompt cache srv load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391 init: chat template, example_format: |im_start|system You are a helpful assistant|im_end| |im_start|user Hello|im_end| |im_start|assistant Hi there|im_end| |im_start|user How are you?|im_end| |im_start|assistant think /think srv init: init: chat template, thinking 0 main: model loaded main: server is listening on http://127.0.0.1:8033 main: starting the main loop... srv update_slots: all slots are idle系统检测到CPU有8个线程用了7个输出一堆参数后等待用浏览器访问http://127.0.0.1:8033。在浏览器中输入问题输出速度比命令行慢一些大约3t/s。服务端输出如下内容srv log_server_r: done request: GET / 127.0.0.1 200 srv params_from_: Chat format: peg-constructed slot get_availabl: id 3 | task -1 | selected slot by LRU, t_last -1 slot launch_slot_: id 3 | task -1 | sampler chain: logits - ?penalties - ?dry - ?top-n-sigma - top-k - ?typical - top-p - min-p - ?xtc - temp-ext - dist slot launch_slot_: id 3 | task 0 | processing task, is_child 0 slot update_slots: id 3 | task 0 | new prompt, n_ctx_slot 262144, n_keep 0, task.n_tokens 23 slot update_slots: id 3 | task 0 | n_tokens 0, memory_seq_rm [0, end) srv log_server_r: done request: POST /v1/chat/completions 127.0.0.1 200 slot init_sampler: id 3 | task 0 | init sampler, took 0.01 ms, tokens: text 23, total 23 slot update_slots: id 3 | task 0 | prompt processing done, n_tokens 23, batch.n_tokens 23 slot print_timing: id 3 | task 0 | prompt eval time 1447.31 ms / 23 tokens ( 62.93 ms per token, 15.89 tokens per second) eval time 171453.86 ms / 569 tokens ( 301.32 ms per token, 3.32 tokens per second) total time 172901.17 ms / 592 tokens slot release: id 3 | task 0 | stop processing: n_tokens 591, truncated 0 srv update_slots: all slots are idle ^Csrv operator(): operator(): cleaning up before exit... llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | llama_memory_breakdown_print: | - Host | 4457 522 3149 786 |