在Android设备上利用Termux安装llama.cpp并启动webui

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 Ctrl+C 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-20=0

我们要解的是方程：
x 2 − x − 20 = 0 x^2 - x - 20 = 0 x2−x−20=0

1. 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 = 0 x2−2x+9−x=0，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 = 0 x2−20+x=0
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)(a+b) a2−b2=(a−b)(a+b)。

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​)2−20=0
这会导致 x x x 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 1 1。
我们需要找到两个数，乘积为 − 20 -20 −20，和为 1 1 1。
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)(x+5)=x2−4x+5x−20=x2+x−20

2. 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)(x+5)=x2+5x−4x−20=x2+x−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 = 0 x2+x−20=0

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 = 0 x−4=0或x+5=0

解得：
x 1 = 4 x_1 = 4 x1​=4
x 2 = − 5 x_2 = -5 x2​=−5

3. 结论

方程的解为：
x = 4  或  x = − 5 x = 4 \text{ 或 } x = -5 x=4 或 x=−5

[ Prompt: 45.1 t/s | Generation: 6.6 t/s ]

/exit

Exiting... 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 bs=512), 1737 (with bs=1) 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 |