看目前這社區越來越多人買7900XTX了，大家為了一個爽度token無限發與反應速度，這幾天折騰的過程分享給大家(win11+vulkan & ubuntu +rocm)
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@chia-an-yang @agi Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf ,请问你们这个模型是在哪下载的，现在hauhuacs的huggingface的repo里面 已经没有这个模型了。google也搜不到。
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@agi 您好！我也是用7900xtx显卡，使用
/usr/local/bin/llama-server
-m ./models/Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf
--mmproj ./models/mmproj-Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-f16.gguf
-c 131072
--parallel 1
-b 2048
-ub 512
-fa 1
-ngl 99
-t 16
--spec-type draft-mtp
--cache-type-k q5_0
--cache-type-v q4_1
--no-mmap
--temp 0.4
--spec-draft-n-max 3
--top-p 0.95
--top-k 20
--host 0.0.0.0
--port 8080
--tools all启动llama.cpp, 但是遇到oom的错误如下：
/usr/local/bin/llama-server -m ./models/Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf --mmproj ./models/mmproj-Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-f16.gguf -c 131072 --parallel 1 -b 2048 -ub 512 -fa 1 -ngl 99 -t 16 --spec-type draft-mtp --cache-type-k q5_0 --cache-type-v q4_1 --no-mmap --temp 0.4 --spec-draft-n-max 3 --top-p 0.95 --top-k 20 --host 0.0.0.0 --port 8080 --tools all
0.00.014.095 I log_info: verbosity = 3 (adjust with the-lv NCLI arg)
0.00.014.097 I device_info:
0.00.014.112 I - ROCm0 : Radeon RX 7900 XTX (24560 MiB, 24524 MiB free)
0.00.014.154 I - ROCm1 : AMD Radeon Graphics (47068 MiB, 89322 MiB free)
0.00.014.156 I - CPU : AMD Ryzen 7 9700X 8-Core Processor (94137 MiB, 94137 MiB free)
0.00.014.207 I system_info: n_threads = 16 (n_threads_batch = 16) / 16 | ROCm : NO_VMM = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
0.00.014.234 I srv init: running without SSL
0.00.014.273 I srv init: using 15 threads for HTTP server
0.00.014.473 W srv llama_server: -----------------
0.00.014.474 W srv llama_server: Built-in tools are enabled, do not expose server to untrusted environments
0.00.014.474 W srv llama_server: This feature is EXPERIMENTAL and may be changed in the future
0.00.014.474 W srv llama_server: -----------------
0.00.014.481 I srv start: binding port with default address family
0.00.015.619 I srv llama_server: loading model
0.00.015.661 I srv load_model: loading model './models/Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf'
0.00.052.136 I srv load_model: [mtmd] estimated worst-case memory usage of mmproj is 1157.64 MiB (took 36.45 ms)
0.00.295.983 I srv load_model: [spec] estimated memory usage of MTP context is 708.02 MiB
0.00.296.004 I common_init_result: fitting params to device memory ...
0.00.296.004 I common_init_result: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)
0.00.517.578 W common_fit_params: failed to fit params to free device memory: n_gpu_layers already set by user to 99, abort
0.01.810.285 W llama_context: n_ctx_seq (131072) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
0.01.838.385 I common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
0.01.916.196 I srv load_model: creating MTP draft context against the target model './models/Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf'
0.01.916.222 W llama_context: n_ctx_seq (131072) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
0.01.932.754 W load_hparams: Qwen-VL models require at minimum 1024 image tokens to function correctly on grounding tasks
0.01.932.756 W load_hparams: if you encounter problems with accuracy, try adding --image-min-tokens 1024
0.01.932.756 W load_hparams: more info: https://github.com/ggml-org/llama.cpp/issues/168420.01.933.558 E ggml_backend_cuda_buffer_type_alloc_buffer: allocating 884.62 MiB on device 0: cudaMalloc failed: out of memory
0.01.933.561 E alloc_tensor_range: failed to allocate ROCm0 buffer of size 927588992
/home/liubo/llama.cpp/ggml/src/ggml-backend.cpp:179: GGML_ASSERT(buffer) failed
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[New LWP 459696]This GDB supports auto-downloading debuginfo from the following URLs:
https://debuginfod.ubuntu.com
Enable debuginfod for this session? (y or [n]) [answered N; input not from terminal]
Debuginfod has been disabled.
To make this setting permanent, add 'set debuginfod enabled off' to .gdbinit.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
0x0000762b61110813 in __GI___wait4 (pid=459889, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
#0 0x0000762b61110813 in __GI___wait4 (pid=459889, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
30 in ../sysdeps/unix/sysv/linux/wait4.c
#1 0x0000762b6134e663 in ggml_print_backtrace () from /home/liubo/llama.cpp/build/bin/libggml-base.so.0
#2 0x0000762b6134e80b in ggml_abort () from /home/liubo/llama.cpp/build/bin/libggml-base.so.0
#3 0x0000762b61367611 in ggml_backend_buffer_set_usage () from /home/liubo/llama.cpp/build/bin/libggml-base.so.0
#4 0x0000762b617a75e8 in clip_model_loader::load_tensors(clip_ctx&) () from /home/liubo/llama.cpp/build/bin/libmtmd.so.0
#5 0x0000762b61795dcd in clip_init(char const*, clip_context_params) () from /home/liubo/llama.cpp/build/bin/libmtmd.so.0
#6 0x0000762b6170987c in mtmd_context::mtmd_context(char const*, llama_model const*, mtmd_context_params const&, bool) () from /home/liubo/llama.cpp/build/bin/libmtmd.so.0
#7 0x0000762b61703211 in mtmd_init_from_file () from /home/liubo/llama.cpp/build/bin/libmtmd.so.0
#8 0x0000762b619aab79 in server_context_impl::load_model(common_params&) () from /home/liubo/llama.cpp/build/bin/libllama-server-impl.so
#9 0x0000762b618e4a48 in llama_server(int, char**) () from /home/liubo/llama.cpp/build/bin/libllama-server-impl.so
#10 0x0000762b6102a1ca in __libc_start_call_main (main=main@entry=0x5e6c5fa22270 <main>, argc=argc@entry=40, argv=argv@entry=0x7fffc3eb01c8) at ../sysdeps/nptl/libc_start_call_main.h:58
warning: 58 ../sysdeps/nptl/libc_start_call_main.h: No such file or directory
#11 0x0000762b6102a28b in __libc_start_main_impl (main=0x5e6c5fa22270 <main>, argc=40, argv=0x7fffc3eb01c8, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffc3eb01b8) at ../csu/libc-start.c:360
warning: 360 ../csu/libc-start.c: No such file or directory
#12 0x00005e6c5fa222a5 in _start ()
[Inferior 1 (process 459658) detached]
Aborted (core dumped)请问是我哪步弄错了吗？我问了gemini,它让我减少上下文，q4我可运行，占用21.5g，我加上q4和q5模型的权重差，我大概差1g的内存。我们几乎是一样的环境。感谢！!
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@agi 您好！我也是用7900xtx显卡，使用
/usr/local/bin/llama-server
-m ./models/Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf
--mmproj ./models/mmproj-Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-f16.gguf
-c 131072
--parallel 1
-b 2048
-ub 512
-fa 1
-ngl 99
-t 16
--spec-type draft-mtp
--cache-type-k q5_0
--cache-type-v q4_1
--no-mmap
--temp 0.4
--spec-draft-n-max 3
--top-p 0.95
--top-k 20
--host 0.0.0.0
--port 8080
--tools all启动llama.cpp, 但是遇到oom的错误如下：
/usr/local/bin/llama-server -m ./models/Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf --mmproj ./models/mmproj-Qwen3.6-27B-Uncensored-HauhauCS-Aggressive-f16.gguf -c 131072 --parallel 1 -b 2048 -ub 512 -fa 1 -ngl 99 -t 16 --spec-type draft-mtp --cache-type-k q5_0 --cache-type-v q4_1 --no-mmap --temp 0.4 --spec-draft-n-max 3 --top-p 0.95 --top-k 20 --host 0.0.0.0 --port 8080 --tools all
0.00.014.095 I log_info: verbosity = 3 (adjust with the-lv NCLI arg)
0.00.014.097 I device_info:
0.00.014.112 I - ROCm0 : Radeon RX 7900 XTX (24560 MiB, 24524 MiB free)
0.00.014.154 I - ROCm1 : AMD Radeon Graphics (47068 MiB, 89322 MiB free)
0.00.014.156 I - CPU : AMD Ryzen 7 9700X 8-Core Processor (94137 MiB, 94137 MiB free)
0.00.014.207 I system_info: n_threads = 16 (n_threads_batch = 16) / 16 | ROCm : NO_VMM = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
0.00.014.234 I srv init: running without SSL
0.00.014.273 I srv init: using 15 threads for HTTP server
0.00.014.473 W srv llama_server: -----------------
0.00.014.474 W srv llama_server: Built-in tools are enabled, do not expose server to untrusted environments
0.00.014.474 W srv llama_server: This feature is EXPERIMENTAL and may be changed in the future
0.00.014.474 W srv llama_server: -----------------
0.00.014.481 I srv start: binding port with default address family
0.00.015.619 I srv llama_server: loading model
0.00.015.661 I srv load_model: loading model './models/Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf'
0.00.052.136 I srv load_model: [mtmd] estimated worst-case memory usage of mmproj is 1157.64 MiB (took 36.45 ms)
0.00.295.983 I srv load_model: [spec] estimated memory usage of MTP context is 708.02 MiB
0.00.296.004 I common_init_result: fitting params to device memory ...
0.00.296.004 I common_init_result: (for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on)
0.00.517.578 W common_fit_params: failed to fit params to free device memory: n_gpu_layers already set by user to 99, abort
0.01.810.285 W llama_context: n_ctx_seq (131072) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
0.01.838.385 I common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
0.01.916.196 I srv load_model: creating MTP draft context against the target model './models/Qwen3.6-27B-Uncensored-HauhauCS-Balanced-MTP-Q5_K_P.gguf'
0.01.916.222 W llama_context: n_ctx_seq (131072) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
0.01.932.754 W load_hparams: Qwen-VL models require at minimum 1024 image tokens to function correctly on grounding tasks
0.01.932.756 W load_hparams: if you encounter problems with accuracy, try adding --image-min-tokens 1024
0.01.932.756 W load_hparams: more info: https://github.com/ggml-org/llama.cpp/issues/168420.01.933.558 E ggml_backend_cuda_buffer_type_alloc_buffer: allocating 884.62 MiB on device 0: cudaMalloc failed: out of memory
0.01.933.561 E alloc_tensor_range: failed to allocate ROCm0 buffer of size 927588992
/home/liubo/llama.cpp/ggml/src/ggml-backend.cpp:179: GGML_ASSERT(buffer) failed
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[New LWP 459696]This GDB supports auto-downloading debuginfo from the following URLs:
https://debuginfod.ubuntu.com
Enable debuginfod for this session? (y or [n]) [answered N; input not from terminal]
Debuginfod has been disabled.
To make this setting permanent, add 'set debuginfod enabled off' to .gdbinit.
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
0x0000762b61110813 in __GI___wait4 (pid=459889, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
warning: 30 ../sysdeps/unix/sysv/linux/wait4.c: No such file or directory
#0 0x0000762b61110813 in __GI___wait4 (pid=459889, stat_loc=0x0, options=0, usage=0x0) at ../sysdeps/unix/sysv/linux/wait4.c:30
30 in ../sysdeps/unix/sysv/linux/wait4.c
#1 0x0000762b6134e663 in ggml_print_backtrace () from /home/liubo/llama.cpp/build/bin/libggml-base.so.0
#2 0x0000762b6134e80b in ggml_abort () from /home/liubo/llama.cpp/build/bin/libggml-base.so.0
#3 0x0000762b61367611 in ggml_backend_buffer_set_usage () from /home/liubo/llama.cpp/build/bin/libggml-base.so.0
#4 0x0000762b617a75e8 in clip_model_loader::load_tensors(clip_ctx&) () from /home/liubo/llama.cpp/build/bin/libmtmd.so.0
#5 0x0000762b61795dcd in clip_init(char const*, clip_context_params) () from /home/liubo/llama.cpp/build/bin/libmtmd.so.0
#6 0x0000762b6170987c in mtmd_context::mtmd_context(char const*, llama_model const*, mtmd_context_params const&, bool) () from /home/liubo/llama.cpp/build/bin/libmtmd.so.0
#7 0x0000762b61703211 in mtmd_init_from_file () from /home/liubo/llama.cpp/build/bin/libmtmd.so.0
#8 0x0000762b619aab79 in server_context_impl::load_model(common_params&) () from /home/liubo/llama.cpp/build/bin/libllama-server-impl.so
#9 0x0000762b618e4a48 in llama_server(int, char**) () from /home/liubo/llama.cpp/build/bin/libllama-server-impl.so
#10 0x0000762b6102a1ca in __libc_start_call_main (main=main@entry=0x5e6c5fa22270 <main>, argc=argc@entry=40, argv=argv@entry=0x7fffc3eb01c8) at ../sysdeps/nptl/libc_start_call_main.h:58
warning: 58 ../sysdeps/nptl/libc_start_call_main.h: No such file or directory
#11 0x0000762b6102a28b in __libc_start_main_impl (main=0x5e6c5fa22270 <main>, argc=40, argv=0x7fffc3eb01c8, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>, stack_end=0x7fffc3eb01b8) at ../csu/libc-start.c:360
warning: 360 ../csu/libc-start.c: No such file or directory
#12 0x00005e6c5fa222a5 in _start ()
[Inferior 1 (process 459658) detached]
Aborted (core dumped)请问是我哪步弄错了吗？我问了gemini,它让我减少上下文，q4我可运行，占用21.5g，我加上q4和q5模型的权重差，我大概差1g的内存。我们几乎是一样的环境。感谢！!
@nami-ryuu 建議vulkan順很多
#!/bin/bash
先鎖 GPU 時脈（需 sudo）
sudo rocm-smi --device 0 --setperflevel manual
sudo bash -c "echo '2' > /sys/class/drm/card2/device/pp_dpm_sclk"
sudo bash -c "echo '3' > /sys/class/drm/card2/device/pp_dpm_mclk"export VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/radeon_icd.json
SERVER=/path/to/llama.cpp/build-vulkan/bin/llama-server
MODEL=/path/to/Qwopus3.6-27B-v2-MTP-IQ4_XS.gguf"$SERVER"
--host 0.0.0.0 --port 8080
--device Vulkan0 \ # 指定 GPU0
-m "$MODEL"
--alias "unsloth/Qwen3.6-27B-GGUF"
--spec-type draft-mtp \ # 開啟 MTP 推測解碼
--spec-draft-n-max 3 \ # 一次預測 3 個草稿 token
-ngl 99 \ # 全部層放 GPU
--ctx-size 65536 \ # 65K context
-n 8192
-b 2048 -ub 512 -np 1
--cache-type-k q8_0 \ # q8_0 KV cache（比 q4_0 接受率高 10-15%）
--cache-type-v q8_0
--no-mmap --mlock
--flash-attn on
--jinja --no-warmup --reasoning off注意：不在 server 層設 sampling 參數（top-k/presence-penalty 會降低 MTP 接受率）
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这个论坛的界面太丑了吧
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@python96998 你可以在随便聊聊板块专门发帖，说出你对论坛UI的感受，可以说出哪里丑，这是你作为访客的权利，也可以提出改进建议。
这是个技术话题的帖子，你在这里如此回帖，是缺乏教养的表现。你不是宇宙的中心，这个论坛不是你的许愿池，如此缺乏教养就会被我扇耳光，被骂然后被禁言。煞笔东西。
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@agi @chia-an-yang 两位老师我跑通了，但是我用hermes的时候工具调用感觉卡了额，我的7900xtx在疯狂的生成，但是hermes却卡住了。请问两位遇到过类似的问题吗？
llama.cpp 输出：65.27 t/s, tg_3s = 55.86 t/s
36.30.567.224 I slot print_timing: id 0 | task 8648 | n_decoded = 62072, tg = 65.24 t/s, tg_3s = 55.84 t/s
36.33.579.807 I slot print_timing: id 0 | task 8648 | n_decoded = 62240, tg = 65.21 t/s, tg_3s = 55.77 t/s
36.36.592.579 I slot print_timing: id 0 | task 8648 | n_decoded = 62408, tg = 65.18 t/s, tg_3s = 55.76 t/s
36.39.607.362 I slot print_timing: id 0 | task 8648 | n_decoded = 62576, tg = 65.15 t/s, tg_3s = 55.73 t/s
36.42.629.501 I slot print_timing: id 0 | task 8648 | n_decoded = 62744, tg = 65.12 t/s, tg_3s = 55.59 t/s
36.45.651.508 I slot print_timing: id 0 | task 8648 | n_decoded = 62912, tg = 65.09 t/s, tg_3s = 55.59 t/s
36.48.669.380 I slot print_timing: id 0 | task 8648 | n_decoded = 63080, tg = 65.06 t/s, tg_3s = 55.67 t/s
36.51.697.721 I slot print_timing: id 0 | task 8648 | n_decoded = 63247, tg = 65.03 t/s, tg_3s = 55.15 t/s
36.54.730.154 I slot print_timing: id 0 | task 8648 | n_decoded = 63415, tg = 65.00 t/s, tg_3s = 55.40 t/s
36.57.762.852 I slot print_timing: id 0 | task 8648 | n_decoded = 63583, tg = 64.97 t/s, tg_3s = 55.40 t/s
37.00.794.845 I slot print_timing: id 0 | task 8648 | n_decoded = 63751, tg = 64.94 t/s, tg_3s = 55.41 t/shermes输出：
c09f0fd3-2890-42e1-838f-8e36a2ab527b-bd93db497055bc01fe89b39dc4f1a308915fe680.rtfd
preparing browser_navigate...
navigate
search.yahoo.com
14.2s- Hermes
Let me try a more targeted search.
A
preparing browser_navigate... navigate www.google.com
3.35
Response truncated (finish_reason='length')
preparing browser_navigate...
navigate duckduckgo.com 20.5s preparing browser_scroll...
↓
scroll
down 0.2s
LOI
preparing browser_snapshot...
snapshot compact 0.2s preparing browser_navigate... navigate duckduckgo.com 1.5s
(>** cogitating...
model hit max output toke - qwen3.6-27b 30,9K/131.1K [
1］24% ｜36m ｜020
- Hermes
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@agi @chia-an-yang 两位老师我跑通了，但是我用hermes的时候工具调用感觉卡了额，我的7900xtx在疯狂的生成，但是hermes却卡住了。请问两位遇到过类似的问题吗？
llama.cpp 输出：65.27 t/s, tg_3s = 55.86 t/s
36.30.567.224 I slot print_timing: id 0 | task 8648 | n_decoded = 62072, tg = 65.24 t/s, tg_3s = 55.84 t/s
36.33.579.807 I slot print_timing: id 0 | task 8648 | n_decoded = 62240, tg = 65.21 t/s, tg_3s = 55.77 t/s
36.36.592.579 I slot print_timing: id 0 | task 8648 | n_decoded = 62408, tg = 65.18 t/s, tg_3s = 55.76 t/s
36.39.607.362 I slot print_timing: id 0 | task 8648 | n_decoded = 62576, tg = 65.15 t/s, tg_3s = 55.73 t/s
36.42.629.501 I slot print_timing: id 0 | task 8648 | n_decoded = 62744, tg = 65.12 t/s, tg_3s = 55.59 t/s
36.45.651.508 I slot print_timing: id 0 | task 8648 | n_decoded = 62912, tg = 65.09 t/s, tg_3s = 55.59 t/s
36.48.669.380 I slot print_timing: id 0 | task 8648 | n_decoded = 63080, tg = 65.06 t/s, tg_3s = 55.67 t/s
36.51.697.721 I slot print_timing: id 0 | task 8648 | n_decoded = 63247, tg = 65.03 t/s, tg_3s = 55.15 t/s
36.54.730.154 I slot print_timing: id 0 | task 8648 | n_decoded = 63415, tg = 65.00 t/s, tg_3s = 55.40 t/s
36.57.762.852 I slot print_timing: id 0 | task 8648 | n_decoded = 63583, tg = 64.97 t/s, tg_3s = 55.40 t/s
37.00.794.845 I slot print_timing: id 0 | task 8648 | n_decoded = 63751, tg = 64.94 t/s, tg_3s = 55.41 t/shermes输出：
c09f0fd3-2890-42e1-838f-8e36a2ab527b-bd93db497055bc01fe89b39dc4f1a308915fe680.rtfd
preparing browser_navigate...
navigate
search.yahoo.com
14.2s- Hermes
Let me try a more targeted search.
A
preparing browser_navigate... navigate www.google.com
3.35
Response truncated (finish_reason='length')
preparing browser_navigate...
navigate duckduckgo.com 20.5s preparing browser_scroll...
↓
scroll
down 0.2s
LOI
preparing browser_snapshot...
snapshot compact 0.2s preparing browser_navigate... navigate duckduckgo.com 1.5s
(>** cogitating...
model hit max output toke - qwen3.6-27b 30,9K/131.1K [
1］24% ｜36m ｜020
- Hermes
-
@terry 老师，但是它在的decode的时候生成将近60000个字符之后系统强制停止的。Response truncated (finish_reason='length')，感觉它不知道啥时候停止，最后hermes把结果截断了。
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@terry 我就问了他一个问题：“吉利领克08车机如何通过adb安装应用？“，deepseek v4 flash 调用的时候感觉也没那么时间长。其实开始的时候挺顺利的，就是到navigate google.com 这个工具调用的时候生成了60000多个字符才被hermes强制截断结束。感觉挺奇怪的。不过这个vulkan比rocm快好多。这个挺好的。
@nami-ryuu 你先用deepseek v4 flash幫你把hermes搜索工具設定好,跟把soul跟memory也寫好搜索的時候要跑哪些工具,避免本地模型調用工具能力不足的地方他會不斷重試跑老半天跑不出來,讓在線雲端api (ds4 flash)幫你把本地的工作流都設計好,之後你就可以爽用本地端的hermes agent,
