Eval bug: MTP with Qwen3.6 27B outputs repeated //// after long session

[aand18]

Name and Version

.\llama-server.exe --version
version: 196 (40d5358)
built with MSVC 19.44.35226.0 for x64

Operating systems

Windows

GGML backends

CUDA

Hardware

Ryzen 7950X3D + RTX 4090 (driver 596.36)

Models

unsloth/Qwen3.6-27B-MTP-GGUF IQ4_XS

-download validated with sha256

Problem description & steps to reproduce

The server started outputting "///////////////" in a loop, after a very long session (hours), in which it functioned correctly and with good performance.

0. build with:
   cmake -B build -DGGML_NATIVE=ON -DGGML_CUDA=ON -DGGML_CUDA_FA_ALL_QUANTS=ON -DLLAMA_BUILD_UI=OFF && cmake --build build --config Release -j 32 --target llama-server

   nvcc --version
   Cuda compilation tools, release 13.1, V13.1.80
   Build cuda_13.1.r13.1/compiler.36836380_0
   

1. run server:

   $env:LLAMA_SERVER_SLOTS_DEBUG=1; llama-server.exe -m unsloth\Qwen3.6-27B-MTP-GGUF\Qwen3.6-27B-IQ4_XS.gguf --host 0.0.0.0 --port 8081 -fa on -ctk q8_0 -ctv q5_1 -ngl 99 -ngld 99 --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --repeat-penalty 1 --presence-penalty 0 -c 148000 --parallel 1 --jinja --chat-template-kwargs '{"preserve_thinking": true}' --metrics --spec-type draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.75 -cram 16000 --log-timestamps --path .\webui\

2. in OpenCode instruct the agent to implement a complicated software project with no intervention (delegate each task to a subagent to conserve context etc.) and let it run

First Bad Commit

No response

Relevant log output

NB: OpenCode has model config output limit at 32k.

Server log snippet (full below) - generation was OK and then it started outputting "////" in a loop until 32k limit

368.34.643.407 W slot update_slots: id  0 | task 145480 |       29     198  248059  248046
368.34.643.407 W slot update_slots: id  0 | task 145480 |       29     198  248059  248046
368.34.643.409 W slot update_slots: id  0 | task 145480 | n_past = 12314, slot.prompt.tokens.size() = 12314, seq_id = 0, pos_min = 12313, n_swa = 0
368.34.643.410 I slot update_slots: id  0 | task 145480 | Checking checkpoint with [12218, 12218] against 12313...
368.34.657.656 W slot update_slots: id  0 | task 145480 | restored context checkpoint (pos_min = 12218, pos_max = 12218, n_tokens = 12219, n_past = 12219, size = 171.487 MiB)
368.34.894.607 I slot create_check: id  0 | task 145480 | created context checkpoint 5 of 32 (pos_min = 12741, pos_max = 12741, n_tokens = 12742, size = 172.423 MiB)
368.35.241.319 I reasoning-budget: deactivated (natural end)
368.35.866.875 I slot print_timing: id  0 | task 145480 | n_decoded =    103, tg = 110.13 t/s
368.37.090.834 I slot print_timing: id  0 | task 145480 | prompt eval time =     288.08 ms /   527 tokens (    0.55 ms per token,  1829.35 tokens per second)
368.37.090.838 I slot print_timing: id  0 | task 145480 |        eval time =    2159.22 ms /   254 tokens (    8.50 ms per token,   117.64 tokens per second)
368.37.090.839 I slot print_timing: id  0 | task 145480 |       total time =    2447.30 ms /   781 tokens
368.37.090.840 I slot print_timing: id  0 | task 145480 |    graphs reused =      52546
368.37.090.841 I slot print_timing: id  0 | task 145480 | draft acceptance = 0.99505 (  201 accepted /   202 generated)
368.37.090.855 I statistics        draft-mtp: #calls(b,g,a) =  566 142602 107915, #gen drafts = 107915, #acc drafts = 103668, #gen tokens = 294686, #acc tokens = 274174, dur(b,g,a) = 0.412, 851457.082, 118.639 ms
368.37.091.409 I slot      release: id  0 | task 145480 | stop processing: n_tokens = 13002, truncated = 0
368.37.091.461 I srv  update_slots: all slots are idle
368.37.263.055 I srv  params_from_: Chat format: peg-native
368.37.264.238 I slot get_availabl: id  0 | task -1 | selected slot by LCP similarity, sim_best = 0.371 (> 0.100 thold), f_keep = 1.000
368.37.265.044 I reasoning-budget: activated, budget=2147483647 tokens
368.37.265.250 I slot launch_slot_: id  0 | task 145539 | processing task, is_child = 0
368.37.265.276 W slot update_slots: id  0 | task 145539 | old: ...
</tool_call><|im_end|>
 | <|endoftext|>
368.37.265.276 W slot update_slots: id  0 | task 145539 | new: ...
</tool_call><|im_end|>
 | <|im_start|>
368.37.265.276 W slot update_slots: id  0 | task 145539 |      198  248059  248046     198  248044
368.37.265.277 W slot update_slots: id  0 | task 145539 |      198  248059  248046     198  248045
368.37.265.279 W slot update_slots: id  0 | task 145539 | n_past = 13001, slot.prompt.tokens.size() = 13002, seq_id = 0, pos_min = 13001, n_swa = 0
368.37.265.279 I slot update_slots: id  0 | task 145539 | Checking checkpoint with [12741, 12741] against 13000...
368.37.278.750 W slot update_slots: id  0 | task 145539 | restored context checkpoint (pos_min = 12741, pos_max = 12741, n_tokens = 12742, n_past = 12742, size = 172.423 MiB)
368.40.199.291 I slot update_slots: id  0 | task 145539 | 8192 tokens since last checkpoint at 12742, creating new checkpoint during processing at position 22982
368.40.226.084 I slot create_check: id  0 | task 145539 | created context checkpoint 6 of 32 (pos_min = 20933, pos_max = 20933, n_tokens = 20934, size = 187.079 MiB)
368.40.991.434 I slot print_timing: id  0 | task 145539 | prompt processing, n_tokens =  10240, progress = 0.66, t =   3.73 s / 2748.13 tokens per second
368.41.770.369 I slot print_timing: id  0 | task 145539 | prompt processing, n_tokens =  12288, progress = 0.71, t =   4.51 s / 2727.57 tokens per second
368.42.564.058 I slot print_timing: id  0 | task 145539 | prompt processing, n_tokens =  14336, progress = 0.77, t =   5.30 s / 2705.52 tokens per second
368.43.372.331 I slot print_timing: id  0 | task 145539 | prompt processing, n_tokens =  16384, progress = 0.83, t =   6.11 s / 2682.79 tokens per second
368.43.372.546 I slot update_slots: id  0 | task 145539 | 8192 tokens since last checkpoint at 20934, creating new checkpoint during processing at position 31174
368.43.398.955 I slot create_check: id  0 | task 145539 | created context checkpoint 7 of 32 (pos_min = 29125, pos_max = 29125, n_tokens = 29126, size = 201.735 MiB)
368.44.218.959 I slot print_timing: id  0 | task 145539 | prompt processing, n_tokens =  18432, progress = 0.89, t =   6.95 s / 2650.68 tokens per second
368.45.050.753 I slot print_timing: id  0 | task 145539 | prompt processing, n_tokens =  20480, progress = 0.95, t =   7.79 s / 2630.54 tokens per second
368.45.590.574 I slot print_timing: id  0 | task 145539 | prompt processing, n_tokens =  21753, progress = 0.99, t =   8.33 s / 2612.88 tokens per second
368.45.619.382 I slot create_check: id  0 | task 145539 | created context checkpoint 8 of 32 (pos_min = 34494, pos_max = 34494, n_tokens = 34495, size = 211.341 MiB)
368.45.835.055 I slot print_timing: id  0 | task 145539 | prompt processing, n_tokens =  22265, progress = 1.00, t =   8.57 s / 2598.08 tokens per second
368.45.865.881 I slot create_check: id  0 | task 145539 | created context checkpoint 9 of 32 (pos_min = 35006, pos_max = 35006, n_tokens = 35007, size = 212.257 MiB)
368.49.934.350 I slot print_timing: id  0 | task 145539 | n_decoded =    100, tg =  24.81 t/s
368.52.965.132 I slot print_timing: id  0 | task 145539 | n_decoded =    175, tg =  24.78 t/s
368.55.974.722 I slot print_timing: id  0 | task 145539 | n_decoded =    249, tg =  24.73 t/s

...

392.06.145.199 I slot print_timing: id  0 | task 145539 | n_decoded =  31920, tg =  22.80 t/s
392.09.171.862 I slot print_timing: id  0 | task 145539 | n_decoded =  31984, tg =  22.79 t/s
392.09.927.219 I slot print_timing: id  0 | task 145539 | prompt eval time =    8638.69 ms / 22269 tokens (    0.39 ms per token,  2577.82 tokens per second)
392.09.927.224 I slot print_timing: id  0 | task 145539 |        eval time = 1404023.25 ms / 32000 tokens (   43.88 ms per token,    22.79 tokens per second)
392.09.927.225 I slot print_timing: id  0 | task 145539 |       total time = 1412661.94 ms / 54269 tokens
392.09.927.225 I slot print_timing: id  0 | task 145539 |    graphs reused =      84415
392.09.927.226 I slot print_timing: id  0 | task 145539 | draft acceptance = 0.00000 (    0 accepted / 127986 generated)
392.09.927.241 I statistics        draft-mtp: #calls(b,g,a) =  567 174600 139913, #gen drafts = 139913, #acc drafts = 103668, #gen tokens = 422672, #acc tokens = 274174, dur(b,g,a) = 0.412, 1117416.901, 152.351 ms
392.09.928.237 I slot      release: id  0 | task 145539 | stop processing: n_tokens = 67010, truncated = 0
392.09.928.350 I srv  update_slots: all slots are idle
392.10.458.829 I srv  params_from_: Chat format: peg-native
392.10.460.468 I slot get_availabl: id  0 | task -1 | selected slot by LRU, t_last = 23529366375
392.10.460.471 I srv  get_availabl: updating prompt cache
392.10.465.357 W srv   prompt_save:  - saving prompt with length 67010, total state size = 2168.536 MiB (draft: 119.887 MiB)
392.10.866.297 I srv          load:  - looking for better prompt, base f_keep = 0.037, sim = 0.044
392.10.866.328 I srv          load:  - found better prompt with f_keep = 1.000, sim = 0.999
392.11.176.092 I srv        update:  - cache state: 1 prompts, 3829.656 MiB (limits: 16000.000 MiB, 148224 tokens, 279962 est)
392.11.176.099 I srv        update:    - prompt 000002142B448BF0:   67010 tokens, checkpoints:  9,  3829.656 MiB
392.11.176.100 I srv  get_availabl: prompt cache update took 715.63 ms
392.11.177.093 I reasoning-budget: activated, budget=2147483647 tokens
392.11.177.383 I slot launch_slot_: id  0 | task 177552 | processing task, is_child = 0
392.11.177.427 W slot update_slots: id  0 | task 177552 | old: ...
</tool_call><|im_end|>

392.11.177.428 W slot update_slots: id  0 | task 177552 | new: ...
</tool_call><|im_end|>

392.11.177.428 W slot update_slots: id  0 | task 177552 |      198  248059  248046     198
392.11.177.429 W slot update_slots: id  0 | task 177552 |      198  248059  248046     198
392.11.177.431 W slot update_slots: id  0 | task 177552 | n_past = 56287, slot.prompt.tokens.size() = 56287, seq_id = 0, pos_min = 56286, n_swa = 0
392.11.177.431 I slot update_slots: id  0 | task 177552 | Checking checkpoint with [55702, 55702] against 56286...
392.11.207.188 W slot update_slots: id  0 | task 177552 | restored context checkpoint (pos_min = 55702, pos_max = 55702, n_tokens = 55703, n_past = 55703, size = 249.284 MiB)
392.11.292.435 W slot create_check: id  0 | task 177552 | erasing old context checkpoint (pos_min = 37194, pos_max = 37194, n_tokens = 37195, size = 216.171 MiB)
392.11.348.400 I slot create_check: id  0 | task 177552 | created context checkpoint 32 of 32 (pos_min = 55830, pos_max = 55830, n_tokens = 55831, size = 249.513 MiB)
392.11.610.000 W slot create_check: id  0 | task 177552 | erasing old context checkpoint (pos_min = 37706, pos_max = 37706, n_tokens = 37707, size = 217.087 MiB)
392.11.656.271 I slot create_check: id  0 | task 177552 | created context checkpoint 32 of 32 (pos_min = 56342, pos_max = 56342, n_tokens = 56343, size = 250.429 MiB)
392.16.164.874 I slot print_timing: id  0 | task 177552 | n_decoded =    100, tg =  22.39 t/s
392.19.185.299 I slot print_timing: id  0 | task 177552 | n_decoded =    167, tg =  22.31 t/s

...

Server init log (verbosity set to 4)

0.00.017.243 I common_params_print_info: build 196 (40d5358d3) with MSVC 19.44.35226.0 for x64
0.00.017.246 I log_info: verbosity = 4 (adjust with the `-lv N` CLI arg)
0.00.017.246 I device_info:
0.00.137.842 I   - CUDA0   : NVIDIA GeForce RTX 4090 (24563 MiB, 23036 MiB free)
0.00.137.849 I   - CPU     : AMD Ryzen 9 7950X3D 16-Core Processor           (64627 MiB, 41570 MiB free)
0.00.137.892 I system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | FA_ALL_QUANTS = 1 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
0.00.137.916 I srv          init: running without SSL
0.00.137.930 I srv          init: using 31 threads for HTTP server
0.00.137.931 I srv          init: The UI is disabled
0.00.137.932 I srv          init: Use --ui/--no-ui (or deprecated --webui/--no-webui) to enable/disable
0.00.138.042 I srv         start: binding port with default address family
0.00.151.012 I srv  llama_server: loading model
0.00.151.028 I srv    load_model: loading model '.\.cache\lm-studio\models\unsloth\Qwen3.6-27B-MTP-GGUF\Qwen3.6-27B-IQ4_XS.gguf'
0.00.151.066 I common_init_result: fitting params to device memory ...
0.00.151.066 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)
0.00.151.070 I common_params_fit_impl: getting device memory data for initial parameters:
0.00.496.782 I common_memory_breakdown_print: | memory breakdown [MiB] | total    free     self   model   context   compute    unaccounted |
0.00.496.786 I common_memory_breakdown_print: |   - CUDA0 (RTX 4090)   | 24563 = 22286 + (19736 = 14285 +    4945 +     505) +      -17459 |
0.00.496.787 I common_memory_breakdown_print: |   - Host               |                    991 =   682 +       0 +     309                |
0.00.539.742 I common_params_fit_impl: projected to use 19736 MiB of device memory vs. 22286 MiB of free device memory
0.00.539.746 I common_params_fit_impl: will leave 2549 >= 1024 MiB of free device memory, no changes needed
0.00.539.749 I common_fit_params: successfully fit params to free device memory
0.00.539.755 I common_fit_params: fitting params to free memory took 0.25 seconds
0.00.588.519 I llama_model_loader: loaded meta data with 52 key-value pairs and 866 tensors from .\.cache\lm-studio\models\unsloth\Qwen3.6-27B-MTP-GGUF\Qwen3.6-27B-IQ4_XS.gguf (version GGUF V3 (latest))
0.00.588.536 I llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
0.00.588.538 I llama_model_loader: - kv   0:                       general.architecture str              = qwen35
0.00.588.539 I llama_model_loader: - kv   1:                               general.type str              = model
0.00.588.540 I llama_model_loader: - kv   2:                     general.sampling.top_k i32              = 20
0.00.588.544 I llama_model_loader: - kv   3:                     general.sampling.top_p f32              = 0.950000
0.00.588.545 I llama_model_loader: - kv   4:                      general.sampling.temp f32              = 1.000000
0.00.588.545 I llama_model_loader: - kv   5:                               general.name str              = Qwen3.6-27B
0.00.588.546 I llama_model_loader: - kv   6:                           general.basename str              = Qwen3.6-27B
0.00.588.546 I llama_model_loader: - kv   7:                       general.quantized_by str              = Unsloth
0.00.588.546 I llama_model_loader: - kv   8:                         general.size_label str              = 27B
0.00.588.547 I llama_model_loader: - kv   9:                            general.license str              = apache-2.0
0.00.588.549 I llama_model_loader: - kv  10:                       general.license.link str              = https://huggingface.co/Qwen/Qwen3.6-2...
0.00.588.549 I llama_model_loader: - kv  11:                           general.repo_url str              = https://huggingface.co/unsloth
0.00.588.550 I llama_model_loader: - kv  12:                   general.base_model.count u32              = 1
0.00.588.551 I llama_model_loader: - kv  13:                  general.base_model.0.name str              = Qwen3.6 27B
0.00.588.551 I llama_model_loader: - kv  14:          general.base_model.0.organization str              = Qwen
0.00.588.552 I llama_model_loader: - kv  15:              general.base_model.0.repo_url str              = https://huggingface.co/Qwen/Qwen3.6-27B
0.00.588.560 I llama_model_loader: - kv  16:                               general.tags arr[str,2]       = ["unsloth", "image-text-to-text"]
0.00.588.561 I llama_model_loader: - kv  17:                         qwen35.block_count u32              = 65
0.00.588.561 I llama_model_loader: - kv  18:                      qwen35.context_length u32              = 262144
0.00.588.562 I llama_model_loader: - kv  19:                    qwen35.embedding_length u32              = 5120
0.00.588.562 I llama_model_loader: - kv  20:                 qwen35.feed_forward_length u32              = 17408
0.00.588.563 I llama_model_loader: - kv  21:                qwen35.attention.head_count u32              = 24
0.00.588.563 I llama_model_loader: - kv  22:             qwen35.attention.head_count_kv u32              = 4
0.00.588.565 I llama_model_loader: - kv  23:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
0.00.588.566 I llama_model_loader: - kv  24:                      qwen35.rope.freq_base f32              = 10000000.000000
0.00.588.567 I llama_model_loader: - kv  25:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
0.00.588.568 I llama_model_loader: - kv  26:                qwen35.attention.key_length u32              = 256
0.00.588.568 I llama_model_loader: - kv  27:              qwen35.attention.value_length u32              = 256
0.00.588.568 I llama_model_loader: - kv  28:                     qwen35.ssm.conv_kernel u32              = 4
0.00.588.569 I llama_model_loader: - kv  29:                      qwen35.ssm.state_size u32              = 128
0.00.588.569 I llama_model_loader: - kv  30:                     qwen35.ssm.group_count u32              = 16
0.00.588.569 I llama_model_loader: - kv  31:                  qwen35.ssm.time_step_rank u32              = 48
0.00.588.570 I llama_model_loader: - kv  32:                      qwen35.ssm.inner_size u32              = 6144
0.00.588.570 I llama_model_loader: - kv  33:             qwen35.full_attention_interval u32              = 4
0.00.588.571 I llama_model_loader: - kv  34:                qwen35.rope.dimension_count u32              = 64
0.00.588.571 I llama_model_loader: - kv  35:                qwen35.nextn_predict_layers u32              = 1
0.00.588.572 I llama_model_loader: - kv  36:                       tokenizer.ggml.model str              = gpt2
0.00.588.572 I llama_model_loader: - kv  37:                         tokenizer.ggml.pre str              = qwen35
0.00.628.257 I llama_model_loader: - kv  38:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
0.00.643.531 I llama_model_loader: - kv  39:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
0.00.683.186 I llama_model_loader: - kv  40:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
0.00.683.190 I llama_model_loader: - kv  41:                tokenizer.ggml.eos_token_id u32              = 248046
0.00.683.191 I llama_model_loader: - kv  42:            tokenizer.ggml.padding_token_id u32              = 248055
0.00.683.191 I llama_model_loader: - kv  43:                tokenizer.ggml.bos_token_id u32              = 248044
0.00.683.192 I llama_model_loader: - kv  44:               tokenizer.ggml.add_bos_token bool             = false
0.00.683.196 I llama_model_loader: - kv  45:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
0.00.683.196 I llama_model_loader: - kv  46:               general.quantization_version u32              = 2
0.00.683.197 I llama_model_loader: - kv  47:                          general.file_type u32              = 30
0.00.683.198 I llama_model_loader: - kv  48:                      quantize.imatrix.file str              = Qwen3.6-27B-GGUF/imatrix_unsloth.gguf
0.00.683.199 I llama_model_loader: - kv  49:                   quantize.imatrix.dataset str              = unsloth_calibration_Qwen3.6-27B.txt
0.00.683.199 I llama_model_loader: - kv  50:             quantize.imatrix.entries_count u32              = 496
0.00.683.200 I llama_model_loader: - kv  51:              quantize.imatrix.chunks_count u32              = 76
0.00.683.200 I llama_model_loader: - type  f32:  456 tensors
0.00.683.201 I llama_model_loader: - type q8_0:    1 tensors
0.00.683.201 I llama_model_loader: - type q4_K:    7 tensors
0.00.683.201 I llama_model_loader: - type q5_K:  113 tensors
0.00.683.202 I llama_model_loader: - type q6_K:    1 tensors
0.00.683.202 I llama_model_loader: - type iq4_xs:  288 tensors
0.00.683.203 I print_info: file format = GGUF V3 (latest)
0.00.683.204 I print_info: file type   = IQ4_XS - 4.25 bpw
0.00.683.207 I print_info: file size   = 14.62 GiB (4.60 BPW)
0.00.683.241 I llama_prepare_model_devices: using device CUDA0 (NVIDIA GeForce RTX 4090) (0000:01:00.0) - 23036 MiB free
0.00.774.052 I load: 0 unused tokens
0.00.799.350 I load: printing all EOG tokens:
0.00.799.354 I load:   - 248044 ('<|endoftext|>')
0.00.799.354 I load:   - 248046 ('<|im_end|>')
0.00.799.354 I load:   - 248063 ('<|fim_pad|>')
0.00.799.355 I load:   - 248064 ('<|repo_name|>')
0.00.799.355 I load:   - 248065 ('<|file_sep|>')
0.00.799.516 I load: special tokens cache size = 33
0.00.837.085 I load: token to piece cache size = 1.7581 MB
0.00.837.098 I print_info: arch                  = qwen35
0.00.837.099 I print_info: vocab_only            = 0
0.00.837.099 I print_info: no_alloc              = 0
0.00.837.100 I print_info: n_ctx_train           = 262144
0.00.837.100 I print_info: n_embd                = 5120
0.00.837.100 I print_info: n_embd_inp            = 5120
0.00.837.101 I print_info: n_layer               = 65
0.00.837.109 I print_info: n_head                = 24
0.00.837.111 I print_info: n_head_kv             = 4
0.00.837.112 I print_info: n_rot                 = 64
0.00.837.112 I print_info: n_swa                 = 0
0.00.837.112 I print_info: is_swa_any            = 0
0.00.837.113 I print_info: n_embd_head_k         = 256
0.00.837.113 I print_info: n_embd_head_v         = 256
0.00.837.114 I print_info: n_gqa                 = 6
0.00.837.116 I print_info: n_embd_k_gqa          = 1024
0.00.837.117 I print_info: n_embd_v_gqa          = 1024
0.00.837.118 I print_info: f_norm_eps            = 0.0e+00
0.00.837.119 I print_info: f_norm_rms_eps        = 1.0e-06
0.00.837.119 I print_info: f_clamp_kqv           = 0.0e+00
0.00.837.119 I print_info: f_max_alibi_bias      = 0.0e+00
0.00.837.120 I print_info: f_logit_scale         = 0.0e+00
0.00.837.120 I print_info: f_attn_scale          = 0.0e+00
0.00.837.120 I print_info: f_attn_value_scale    = 0.0000
0.00.837.122 I print_info: n_ff                  = 17408
0.00.837.122 I print_info: n_expert              = 0
0.00.837.122 I print_info: n_expert_used         = 0
0.00.837.123 I print_info: n_expert_groups       = 0
0.00.837.123 I print_info: n_group_used          = 0
0.00.837.123 I print_info: causal attn           = 1
0.00.837.124 I print_info: pooling type          = -1
0.00.837.124 I print_info: rope type             = 40
0.00.837.124 I print_info: rope scaling          = linear
0.00.837.125 I print_info: freq_base_train       = 10000000.0
0.00.837.126 I print_info: freq_scale_train      = 1
0.00.837.126 I print_info: n_ctx_orig_yarn       = 262144
0.00.837.126 I print_info: rope_yarn_log_mul     = 0.0000
0.00.837.126 I print_info: rope_finetuned        = unknown
0.00.837.127 I print_info: mrope sections        = [11, 11, 10, 0]
0.00.837.127 I print_info: ssm_d_conv            = 4
0.00.837.127 I print_info: ssm_d_inner           = 6144
0.00.837.128 I print_info: ssm_d_state           = 128
0.00.837.128 I print_info: ssm_dt_rank           = 48
0.00.837.128 I print_info: ssm_n_group           = 16
0.00.837.128 I print_info: ssm_dt_b_c_rms        = 0
0.00.837.129 I print_info: model type            = 27B
0.00.837.129 I print_info: model params          = 27.32 B
0.00.837.130 I print_info: general.name          = Qwen3.6-27B
0.00.837.130 I print_info: vocab type            = BPE
0.00.837.131 I print_info: n_vocab               = 248320
0.00.837.131 I print_info: n_merges              = 247587
0.00.837.131 I print_info: BOS token             = 248044 '<|endoftext|>'
0.00.837.132 I print_info: EOS token             = 248046 '<|im_end|>'
0.00.837.132 I print_info: EOT token             = 248046 '<|im_end|>'
0.00.837.132 I print_info: PAD token             = 248055 '<|vision_pad|>'
0.00.837.133 I print_info: LF token              = 198 'Ċ'
0.00.837.133 I print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
0.00.837.133 I print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
0.00.837.133 I print_info: FIM MID token         = 248061 '<|fim_middle|>'
0.00.837.134 I print_info: FIM PAD token         = 248063 '<|fim_pad|>'
0.00.837.134 I print_info: FIM REP token         = 248064 '<|repo_name|>'
0.00.837.134 I print_info: FIM SEP token         = 248065 '<|file_sep|>'
0.00.837.134 I print_info: EOG token             = 248044 '<|endoftext|>'
0.00.837.135 I print_info: EOG token             = 248046 '<|im_end|>'
0.00.837.135 I print_info: EOG token             = 248063 '<|fim_pad|>'
0.00.837.135 I print_info: EOG token             = 248064 '<|repo_name|>'
0.00.837.136 I print_info: EOG token             = 248065 '<|file_sep|>'
0.00.837.136 I print_info: max token length      = 256
0.00.837.137 I load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
0.00.943.996 I load_tensors: offloading output layer to GPU
0.00.944.000 I load_tensors: offloading 64 repeating layers to GPU
0.00.944.000 I load_tensors: offloaded 66/66 layers to GPU
0.00.944.006 I load_tensors:   CPU_Mapped model buffer size =   682.03 MiB
0.00.944.007 I load_tensors:        CUDA0 model buffer size = 14285.76 MiB
...........................................................................................
0.04.695.711 I common_init_result: added <|endoftext|> logit bias = -inf
0.04.695.714 I common_init_result: added <|im_end|> logit bias = -inf
0.04.695.715 I common_init_result: added <|fim_pad|> logit bias = -inf
0.04.695.715 I common_init_result: added <|repo_name|> logit bias = -inf
0.04.695.716 I common_init_result: added <|file_sep|> logit bias = -inf
0.04.696.046 I llama_context: constructing llama_context
0.04.696.049 I llama_context: n_seq_max     = 1
0.04.696.049 I llama_context: n_ctx         = 148224
0.04.696.049 I llama_context: n_ctx_seq     = 148224
0.04.696.050 I llama_context: n_batch       = 2048
0.04.696.050 I llama_context: n_ubatch      = 512
0.04.696.050 I llama_context: causal_attn   = 1
0.04.696.050 I llama_context: flash_attn    = enabled
0.04.696.051 I llama_context: kv_unified    = false
0.04.696.053 I llama_context: freq_base     = 10000000.0
0.04.696.054 I llama_context: freq_scale    = 1
0.04.696.054 I llama_context: n_rs_seq      = 4
0.04.696.054 W llama_context: n_ctx_seq (148224) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
0.04.696.323 I llama_context:  CUDA_Host  output buffer size =     0.95 MiB
0.04.703.360 I llama_kv_cache:      CUDA0 KV buffer size =  4197.75 MiB
0.04.791.920 I llama_kv_cache: size = 4197.75 MiB (148224 cells,  16 layers,  1/1 seqs), K (q8_0): 2460.75 MiB, V (q5_1): 1737.00 MiB
0.04.791.928 I llama_kv_cache: attn_rot_k = 1, n_embd_head_k_all = 256
0.04.791.928 I llama_kv_cache: attn_rot_v = 1, n_embd_head_k_all = 256
0.04.809.433 I llama_memory_recurrent:      CUDA0 RS buffer size =   748.12 MiB
0.04.809.442 I llama_memory_recurrent: size =  748.12 MiB (     1 cells,  65 layers,  1 seqs  4 rs_seq), R (f32):   28.12 MiB, S (f32):  720.00 MiB
0.04.809.447 I sched_reserve: reserving ...
0.04.811.569 I sched_reserve: resolving fused Gated Delta Net support:
0.04.812.739 I sched_reserve: fused Gated Delta Net (autoregressive) enabled
0.04.813.639 I sched_reserve: fused Gated Delta Net (chunked) enabled
0.04.836.151 I sched_reserve:      CUDA0 compute buffer size =   505.00 MiB
0.04.836.156 I sched_reserve:  CUDA_Host compute buffer size =   309.79 MiB
0.04.836.157 I sched_reserve: graph nodes  = 5096
0.04.836.157 I sched_reserve: graph splits = 2
0.04.836.158 I sched_reserve: reserve took 26.71 ms, sched copies = 1
0.04.836.421 I common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
0.04.937.146 I srv    load_model: creating MTP draft context against the target model '.\.cache\lm-studio\models\unsloth\Qwen3.6-27B-MTP-GGUF\Qwen3.6-27B-IQ4_XS.gguf'
0.04.937.186 I llama_context: constructing llama_context
0.04.937.189 I llama_context: n_seq_max     = 1
0.04.937.189 I llama_context: n_ctx         = 148224
0.04.937.189 I llama_context: n_ctx_seq     = 148224
0.04.937.189 I llama_context: n_batch       = 2048
0.04.937.190 I llama_context: n_ubatch      = 512
0.04.937.190 I llama_context: causal_attn   = 1
0.04.937.190 I llama_context: flash_attn    = enabled
0.04.937.191 I llama_context: kv_unified    = false
0.04.937.193 I llama_context: freq_base     = 10000000.0
0.04.937.194 I llama_context: freq_scale    = 1
0.04.937.194 I llama_context: n_rs_seq      = 0
0.04.937.194 W llama_context: n_ctx_seq (148224) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
0.04.937.241 I llama_context:  CUDA_Host  output buffer size =     0.95 MiB
0.04.938.679 I llama_kv_cache:      CUDA0 KV buffer size =   262.36 MiB
0.04.944.075 I llama_kv_cache: size =  262.36 MiB (148224 cells,   1 layers,  1/1 seqs), K (q8_0):  153.80 MiB, V (q5_1):  108.56 MiB
0.04.944.081 I llama_kv_cache: attn_rot_k = 1, n_embd_head_k_all = 256
0.04.944.081 I llama_kv_cache: attn_rot_v = 1, n_embd_head_k_all = 256
0.04.944.189 I sched_reserve: reserving ...
0.04.946.187 I sched_reserve: resolving fused Gated Delta Net support:
0.04.946.301 I sched_reserve: fused Gated Delta Net (autoregressive) enabled
0.04.946.354 I sched_reserve: fused Gated Delta Net (chunked) enabled
0.04.963.635 I sched_reserve:      CUDA0 compute buffer size =   505.00 MiB
0.04.963.640 I sched_reserve:  CUDA_Host compute buffer size =   309.79 MiB
0.04.963.641 I sched_reserve: graph nodes  = 63
0.04.963.641 I sched_reserve: graph splits = 2
0.04.963.641 I sched_reserve: reserve took 19.45 ms, sched copies = 1
0.04.973.061 I srv    load_model: initializing slots, n_slots = 1
0.05.000.968 I common_context_can_seq_rm: the context supports bounded partial sequence removal
0.05.031.564 I common_speculative_impl_draft_mtp: adding speculative implementation 'draft-mtp'
0.05.031.572 I common_speculative_impl_draft_mtp: - n_max=4, n_min=0, p_min=0.75, n_embd=5120, backend_sampling=1
0.05.031.575 I common_speculative_impl_draft_mtp: - gpu_layers=99, cache_k=f16, cache_v=f16, ctx_tgt=yes, ctx_dft=yes, devices=[default]
0.05.031.671 I srv    load_model: speculative decoding context initialized
0.05.031.672 I slot   load_model: id  0 | task -1 | new slot, n_ctx = 148224
0.05.031.683 W srv    load_model: LLAMA_SERVER_SLOTS_DEBUG = 1
0.05.031.732 I srv    load_model: prompt cache is enabled, size limit: 16000 MiB
0.05.031.732 I srv    load_model: use `--cache-ram 0` to disable the prompt cache
0.05.031.732 I srv    load_model: for more info see https://github.com/ggml-org/llama.cpp/pull/16391
0.05.031.748 W srv          init: --cache-idle-slots requires --kv-unified, disabling
0.05.048.908 I init: chat template, example_format: '<|im_start|>system
You are a helpful assistant<|im_end|>
<|im_start|>user
Hello<|im_end|>
<|im_start|>assistant
<think>

</think>

Hi there<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
<think>
'
0.05.061.645 I srv          init: init: chat template, thinking = 1
0.05.061.658 I srv  llama_server: model loaded
0.05.061.660 I srv  llama_server: server is listening on http://0.0.0.0:8081
0.05.061.671 I srv  update_slots: all slots are idle
0.08.535.011 I srv   operator (): operator (): cleaning up before exit...
0.08.536.236 I common_memory_breakdown_print: | memory breakdown [MiB] | total   free     self   model   context   compute    unaccounted |
0.08.536.239 I common_memory_breakdown_print: |   - CUDA0 (RTX 4090)   | 24563 = 2065 + (19736 = 14285 +    4945 +     505) +        2760 |
0.08.536.239 I common_memory_breakdown_print: |   - Host               |                   991 =   682 +       0 +     309                |

Console copy-paste from Powershell (~19k lines):

llama server MTP loop console log.txt

[kcksteve]

I am seeing the same issue. I am running on linux using an amd card and the vulkan backend. I see this with and without MTP, I also seen it on qwen3.5/3.6 both MOE and dense variants. I am mainly using unsloth q4_k_xl or q5_k_m with no kv quantization.

To fix this issue I have to restart the service as the server becomes unresponsive.
If I resume the chat in my agent the issue will reappear.

[redthing1]

I also see the same issue. Qwen 3.6 27B, AMD card, ROCm backend, with and without MTP.

[aand18]

@redthing1 @kcksteve maybe post your versions, model, full command to help debugging

My update is I've seen it happen a few times since then, with the 4 bit XL quant, too.

Once it actually recovered for a few phrases. I noticed //// and stopped the generation in OpenCode. I replied, it continued responding with /////. I then hit "undo" on the last message in OpenCode and basically re-played the last turn. It then generated good output for a little bit, but then had a tool call or whatever, and broke again, /////. I'm experimenting with ngram-mod and MTP so maybe it was a cache thing.
I'll try and recover the logs for that.

Also, I've noticed it never starts in the middle of the generation, always from the beginning.

[aand18]

Just happened again, I've got a whopping 780 t/s of ////////////////. I think ngram-mod sped it up. The positive part is it hits the 32k limit in OpenCode faster and wastes less electricity.

CLI: $env:LLAMA_SERVER_SLOTS_DEBUG=1; llama-server.exe -m unsloth\Qwen3.6-27B-MTP-GGUF\Qwen3.6-27B-UD-Q4_K_XL.gguf --mmproj unsloth\Qwen3.6-27B-MTP-GGUF\mmproj-BF16.gguf --threads 28 --host 0.0.0.0 --port 8081 -fa on -ctk q5_1 -ctv q4_1 -ngl 99 -ngld 99 --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0 --repeat-penalty 1 --presence-penalty 0 -fitt 900 --no-mmap --parallel 1 --jinja --chat-template-kwargs '{"preserve_thinking": true}' --chat-template-file froggeric_v19_chat_template.jinja --metrics --spec-type ngram-mod,draft-mtp --spec-draft-n-max 4 --spec-draft-p-min 0.75 --spec-ngram-mod-n-match 24 --spec-ngram-mod-n-min 48 --spec-ngram-mod-n-max 64 -cram 23000 --log-timestamps --ui --path .\Downloads\llama.cpp-mtp\build\tools\ui\dist\ --log-file $env:temp\lama-server-log.$(get-date -f yyyyMMddTHHmmssZ).txt -lv 4

Full log:

lama-server-log.20260529T214902Z.txt

[aand18]

OpenCode automatic session compaction seems to make it recover for a while:

///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

--------------------- Session Compacted -----------------------------

The user wants me to create a summary of the conversation. Let me compile the key information:

1. Goal: Fix (....)
2. The device (....)
3. I've identified issues (....)
4. I need to fix (....)

Goal
Fix overflow issues with value fields (....)
(....)

[Rogn4r]

Same issue with Qwen3.6-35B (MXFP4).

After a long reasoning session, the model completely breaks and gets stuck in an infinite //// loop. The logs show that draft acceptance drops to 0.00000, causing the main model and MTP draft to desync during context checkpoint restoration.

This is purely a CUDA-specific bug (compiled with Flash Attention). When switching to the Vulkan backend, the exact same model and command work flawlessly. On Vulkan, the draft acceptance stays rock-solid at ~0.89 even after generating 12k+ tokens of continuous reasoning, with context depth scaling past 32k tokens without a single slash.

System:

• CachyOS
• Driver: 595
• CUDA: 13.2
• GPU: 2x rtx pro 2000 blackwell
  Command:
  ./llama-server -hf unsloth/Qwen3.6-35B-A3B-MTP-GGUF:MXFP4_MOE -ctk q8_0 -ctv q8_0 -dio -ngl 999 -fa on -np 1 --spec-type draft-mtp --spec-draft-n-max 2 -c 262144 --kv-unified --image-min-tokens 1024 --spec-draft-type-k q8_0 --spec-draft-type-v q8_0 --no-slots

[aand18]

CUDA: 13.2

@Rogn4r this version is notorious for generating corrupt output, try downgrading.

[Rogn4r]

this version is notorious for generating corrupt output, try downgrading.

Actually, a minor update within the 13.2 series solved the issue. It's working fine now.

[ggerganov]

For people still experiencing the repetition of ////////// with Qwen3.6, could you share your hardware, CUDA/ROCm version and llama.cpp version.

[AbdulrahmanHashem]

this problem went away by itself, i put millions of tokens through qwen3.6 dense and it doen't happen for me anymore.

[ggerganov]

@AbdulrahmanHashem Thanks for the feedback. For my information, what hardware and software version are you using?

[AbdulrahmanHashem]

GPUs: 5060Ti 16GB + 2060 Super
CPU: Ryzen 3600
Ram: 32GB DDR4

[julien-c]

Here's my report (AI-assisted). Reproducing script is hammer.py

Eval bug: ROCm/HIP flash-attention on gfx90a (MI250X) permanently corrupts server state under prompt-cache reuse — all subsequent output becomes /////

Summary

Running llama serve on AMD MI250X (gfx90a, ROCm 6.3), the server starts producing an endless run of a single garbage token for every request after a few minutes of ordinary mixed chat traffic (/ with Qwen3.6-27B, <unused49> with gemma-4-26B-A4B — reproduced with both a hybrid-recurrent and a pure-attention model). Once triggered, the corruption is global and permanent for the process: all 4 slots, raw /completion, novel prompts, temperature: 0, cache_prompt: false — everything returns //////... until the process is restarted.

--flash-attn off fully eliminates the problem. Everything else we bisected does not (table below).

The trigger appears to be re-prefill of a partially matched cached prompt (slot reuse with n_past > 0 after cache truncation): single-shot CLI runs with FA enabled are always correct, and a server hammered only with identical repeated prompts (100% cache hits) never corrupts. The permanence strongly suggests an out-of-bounds VRAM write from the FA kernel landing in long-lived allocations (weights / KV pool), not just a wrong attention result.

Environment

llama.cpp	b9491 (9e58d4d69)
GPUs	8× AMD Instinct MI250X (gfx90a, CDNA2) — also reproduces on a single GPU
ROCm / HIP	6.3.0 (HIP 6.3.42131)
OS	Ubuntu 24.04.3, kernel 6.8.0-79-generic
CPU	AMD EPYC 7763
Build	GGML_HIP=ON, GPU_TARGETS=gfx90a, GGML_HIP_ROCWMMA_FATTN=OFF, GGML_HIP_GRAPHS=ON, GGML_HIP_MMQ_MFMA=ON, GGML_HIP_NO_VMM=ON, Release
Model	ggml-org/Qwen3.6-27B-GGUF:Q8_0 (hybrid attention + recurrent/delta-net layers, iSWA)

Hardware checks out: ECC counters all zero across all 8 GPUs, no amdgpu errors in dmesg, and the GGUF generates correctly on CPU.

Reproduction

Server:

llama serve --host 127.0.0.1 --port 9913 \
  --hf-repo ggml-org/Qwen3.6-27B-GGUF:Q8_0 --n-gpu-layers all

Traffic: the attached hammer.py (stdlib-only) cycles production-like events — a growing multi-turn conversation, a prefix-sharing follow-up question, 4-way parallel bursts, a mid-stream client disconnect, slot-swap pressure, >10 s idle gaps — and after every round sends a temp-0 canary (/completion, "The capital of France is", expecting Paris):

python3 hammer.py 9913 trial 60

Corruption appears within 5–11 rounds (≈30–60 s) in every run with default settings; the canary flips from ' Paris.' to '////' and never recovers. A reduced variant running only the conversation + follow-up + canary (no parallelism, no cancels, no slot swaps, no idles) also corrupts:

python3 hammer.py 9913 trial 60 "parallel,cancel,slot-swap,idle"

Sending only the identical canary 80× in a row stays clean, so decode itself is fine — partial prefix reuse seems required.

Bisection results

Each trial = fresh server, same hammer traffic, verdict by canary:

Configuration	Verdict
default	CORRUPTED (round 6)
canary-only traffic (full cache hits)	CLEAN (80 rounds)
no mid-stream cancels / no parallel / no slot-swap / no idle gaps	CORRUPTED
GGML_CUDA_DISABLE_GRAPHS=1	CORRUPTED
--cache-ram 0	CORRUPTED
--ctx-checkpoints 0	CORRUPTED
--parallel 1	CORRUPTED
-fit off -c 32768	CORRUPTED
--swa-full	CORRUPTED
ROCR_VISIBLE_DEVICES=0 (single GPU)	CORRUPTED
GGML_CUDA_DISABLE_FUSION=1	CORRUPTED
single GPU + fusion disabled	CORRUPTED
CPU only (-ngl 0)	CLEAN (30 rounds)
--flash-attn off	CLEAN (60 rounds)
control: ggml-org/gemma-4-26B-A4B-it-GGUF:Q8_0 (pure attention + iSWA, no recurrent layers), FA on	CORRUPTED (round 17)

Post-corruption behavior

Once triggered (probing the corrupted instance):

• raw /completion, temp 0, fresh prompt → '//////'
• cache_prompt: false → '//////'
• all 4 slots in parallel, distinct fresh prompts → all '////...'
• persists indefinitely; only a process restart clears it

This happened first in production: the instance served short answers normally for ~45 min of sparse traffic, then a follow-up request that restored cached context went into a runaway ///// generation (5832 tokens before the client cancelled), and the instance never produced correct output again.

Notes

• gfx90a with GGML_HIP_ROCWMMA_FATTN=OFF → this is the tile/vec FA fallback path on CDNA2.
• Not specific to hybrid/recurrent models: a pure-attention control (gemma-4-26B-A4B-it, MoE with interleaved SWA) corrupts the same way under the same traffic — its canary flips to an endless run of <unused49> instead of / (different vocab, same single-repeated-token signature, equally permanent across all slots). In every corrupting run, for both models, the corruption first appears on the round immediately following a partial-prefix re-prefill of a previously cached conversation. Both affected models use interleaved SWA, which makes cache-truncation re-prefill frequent; a non-SWA pure-attention model has not been tested.
• Generation throughput with --flash-attn off is unchanged at short context (~34 t/s tg for 27B Q8_0 across 8 GPUs).

Happy to run instrumented builds / further bisection on this machine — the repro is fast and deterministic.

[kcksteve]

HW:
Xeon e5-2696v3
64gb ram
Radeon w6800

SW:
Llama vulkan backend b9496
Cachyos 7.0.10

Setting flash attention off and will see if that stops the issue.

*update
With flash attention off I resumed a coding agent session that failed with repeating ////. This would reproduce the error before but did not this time.

I renabled flash attention as my tg dropped from mid 40s to around 9tps. I may try the rocm backend and see if that runs better with fa off.