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Source: Web Fetch
---
# llama.cpp/tools/llama-bench

Performance testing tool for llama.cpp.

## Table of contents

1. [Syntax](#syntax)
2. [Examples](#examples)
    1. [Text generation with different models](#text-generation-with-different-models)
    2. [Prompt processing with different batch sizes](#prompt-processing-with-different-batch-sizes)
    3. [Different numbers of threads](#different-numbers-of-threads)
    4. [Different numbers of layers offloaded to the GPU](#different-numbers-of-layers-offloaded-to-the-gpu)
3. [Output formats](#output-formats)
    1. [Markdown](#markdown)
    2. [CSV](#csv)
    3. [JSON](#json)
    4. [JSONL](#jsonl)
    5. [SQL](#sql)

## Syntax

```
usage: llama-bench [options]

options:
  -h, --help
  --numa <distribute|isolate|numactl>       numa mode (default: disabled)
  -r, --repetitions <n>                     number of times to repeat each test (default: 5)
  --prio <0|1|2|3>                          process/thread priority (default: 0)
  --delay <0...N> (seconds)                 delay between each test (default: 0)
  -o, --output <csv|json|jsonl|md|sql>      output format printed to stdout (default: md)
  -oe, --output-err <csv|json|jsonl|md|sql> output format printed to stderr (default: none)
  --list-devices                            list available devices and exit
  -v, --verbose                             verbose output
  --progress                                print test progress indicators
  -rpc, --rpc <rpc_servers>                 register RPC devices (comma separated)

test parameters:
  -m, --model <filename>                    (default: models/7B/ggml-model-q4_0.gguf)
  -p, --n-prompt <n>                        (default: 512)
  -n, --n-gen <n>                           (default: 128)
  -pg <pp,tg>                               (default: )
  -d, --n-depth <n>                         (default: 0)
  -b, --batch-size <n>                      (default: 2048)
  -ub, --ubatch-size <n>                    (default: 512)
  -ctk, --cache-type-k <t>                  (default: f16)
  -ctv, --cache-type-v <t>                  (default: f16)
  -t, --threads <n>                         (default: system dependent)
  -C, --cpu-mask <hex,hex>                  (default: 0x0)
  --cpu-strict <0|1>                        (default: 0)
  --poll <0...100>                          (default: 50)
  -ngl, --n-gpu-layers <n>                  (default: 99)
  -ncmoe, --n-cpu-moe <n>                   (default: 0)
  -sm, --split-mode <none|layer|row>        (default: layer)
  -mg, --main-gpu <i>                       (default: 0)
  -nkvo, --no-kv-offload <0|1>              (default: 0)
  -fa, --flash-attn <0|1>                   (default: 0)
  -dev, --device <dev0/dev1/...>            (default: auto)
  -mmp, --mmap <0|1>                        (default: 1)
  -embd, --embeddings <0|1>                 (default: 0)
  -ts, --tensor-split <ts0/ts1/..>          (default: 0)
  -ot --override-tensors <tensor name pattern>=<buffer type>;...
                                            (default: disabled)
  -nopo, --no-op-offload <0|1>              (default: 0)

Multiple values can be given for each parameter by separating them with ','
or by specifying the parameter multiple times. Ranges can be given as
'first-last' or 'first-last+step' or 'first-last*mult'.
```

llama-bench can perform three types of tests:

- Prompt processing (pp): processing a prompt in batches (`-p`)
- Text generation (tg): generating a sequence of tokens (`-n`)
- Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens (`-pg`)

With the exception of `-r`, `-o` and `-v`, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. `-n 16,32`), or the option can be specified multiple times (e.g. `-n 16 -n 32`).

Each test is repeated the number of times given by `-r`, and the results are averaged. The results are given in average tokens per second (t/s) and standard deviation. Some output formats (e.g. json) also include the individual results of each repetition.

Using the `-d <n>` option, each test can be run at a specified context depth, prefilling the KV cache with `<n>` tokens.

For a description of the other options, see the [completion example](../completion/README.md).

> [!NOTE]
> The measurements with `llama-bench` do not include the times for tokenization and for sampling.

## Examples

### Text generation with different models

```sh
$ ./llama-bench -m models/7B/ggml-model-q4_0.gguf -m models/13B/ggml-model-q4_0.gguf -p 0 -n 128,256,512
```

| model                          |       size |     params | backend    | ngl | test       |              t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 | tg 128     |    132.19 ± 0.55 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 | tg 256     |    129.37 ± 0.54 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 | tg 512     |    123.83 ± 0.25 |
| llama 13B mostly Q4_0          |   6.86 GiB |    13.02 B | CUDA       |  99 | tg 128     |     82.17 ± 0.31 |
| llama 13B mostly Q4_0          |   6.86 GiB |    13.02 B | CUDA       |  99 | tg 256     |     80.74 ± 0.23 |
| llama 13B mostly Q4_0          |   6.86 GiB |    13.02 B | CUDA       |  99 | tg 512     |     78.08 ± 0.07 |

### Prompt processing with different batch sizes

```sh
$ ./llama-bench -n 0 -p 1024 -b 128,256,512,1024
```

| model                          |       size |     params | backend    | ngl |    n_batch | test       |              t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------: | ---------- | ---------------: |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 |        128 | pp 1024    |   1436.51 ± 3.66 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 |        256 | pp 1024    |  1932.43 ± 23.48 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 |        512 | pp 1024    |  2254.45 ± 15.59 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 |       1024 | pp 1024    |  2498.61 ± 13.58 |

### Different numbers of threads

```sh
$ ./llama-bench -n 0 -n 16 -p 64 -t 1,2,4,8,16,32
```

| model                          |       size |     params | backend    |    threads | test       |              t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ---------: | ---------- | ---------------: |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |          1 | pp 64      |      6.17 ± 0.07 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |          1 | tg 16      |      4.05 ± 0.02 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |          2 | pp 64      |     12.31 ± 0.13 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |          2 | tg 16      |      7.80 ± 0.07 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |          4 | pp 64      |     23.18 ± 0.06 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |          4 | tg 16      |     12.22 ± 0.07 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |          8 | pp 64      |     32.29 ± 1.21 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |          8 | tg 16      |     16.71 ± 0.66 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |         16 | pp 64      |     33.52 ± 0.03 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |         16 | tg 16      |     15.32 ± 0.05 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |         32 | pp 64      |     59.00 ± 1.11 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CPU        |         32 | tg 16      |     16.41 ± 0.79 |

### Different numbers of layers offloaded to the GPU

```sh
$ ./llama-bench -ngl 10,20,30,31,32,33,34,35
```

| model                          |       size |     params | backend    | ngl | test       |              t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  10 | pp 512     |    373.36 ± 2.25 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  10 | tg 128     |     13.45 ± 0.93 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  20 | pp 512     |    472.65 ± 1.25 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  20 | tg 128     |     21.36 ± 1.94 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  30 | pp 512     |   631.87 ± 11.25 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  30 | tg 128     |     40.04 ± 1.82 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  31 | pp 512     |    657.89 ± 5.08 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  31 | tg 128     |     48.19 ± 0.81 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  32 | pp 512     |    688.26 ± 3.29 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  32 | tg 128     |     54.78 ± 0.65 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  33 | pp 512     |    704.27 ± 2.24 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  33 | tg 128     |     60.62 ± 1.76 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  34 | pp 512     |    881.34 ± 5.40 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  34 | tg 128     |     71.76 ± 0.23 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  35 | pp 512     |   2400.01 ± 7.72 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  35 | tg 128     |    131.66 ± 0.49 |

### Different prefilled context

```
$ ./llama-bench -d 0,512
```

| model                          |       size |     params | backend    | ngl |            test |                  t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen2 7B Q4_K - Medium         |   4.36 GiB |     7.62 B | CUDA       |  99 |           pp512 |      7340.20 ± 23.45 |
| qwen2 7B Q4_K - Medium         |   4.36 GiB |     7.62 B | CUDA       |  99 |           tg128 |        120.60 ± 0.59 |
| qwen2 7B Q4_K - Medium         |   4.36 GiB |     7.62 B | CUDA       |  99 |    pp512 @ d512 |      6425.91 ± 18.88 |
| qwen2 7B Q4_K - Medium         |   4.36 GiB |     7.62 B | CUDA       |  99 |    tg128 @ d512 |        116.71 ± 0.60 |

## Output formats

By default, llama-bench outputs the results in markdown format. The results can be output in other formats by using the `-o` option.

### Markdown

```sh
$ ./llama-bench -o md
```

| model                          |       size |     params | backend    | ngl | test       |              t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 | pp 512     |  2368.80 ± 93.24 |
| llama 7B mostly Q4_0           |   3.56 GiB |     6.74 B | CUDA       |  99 | tg 128     |    131.42 ± 0.59 |

### CSV

```sh
$ ./llama-bench -o csv
```

```csv
build_commit,build_number,cpu_info,gpu_info,backends,model_filename,model_type,model_size,model_n_params,n_batch,n_ubatch,n_threads,cpu_mask,cpu_strict,poll,type_k,type_v,n_gpu_layers,split_mode,main_gpu,no_kv_offload,flash_attn,tensor_split,use_mmap,embeddings,n_prompt,n_gen,n_depth,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
"8cf427ff","5163","AMD Ryzen 7 7800X3D 8-Core Processor","NVIDIA GeForce RTX 4080","CUDA","models/Qwen2.5-7B-Instruct-Q4_K_M.gguf","qwen2 7B Q4_K - Medium","4677120000","7615616512","2048","512","8","0x0","0","50","f16","f16","99","layer","0","0","0","0.00","1","0","512","0","0","2025-04-24T11:57:09Z","70285660","982040","7285.676949","100.064434"
"8cf427ff","5163","AMD Ryzen 7 7800X3D 8-Core Processor","NVIDIA GeForce RTX 4080","CUDA","models/Qwen2.5-7B-Instruct-Q4_K_M.gguf","qwen2 7B Q4_K - Medium","4677120000","7615616512","2048","512","8","0x0","0","50","f16","f16","99","layer","0","0","0","0.00","1","0","0","128","0","2025-04-24T11:57:10Z","1067431600","3834831","119.915244","0.430617"
```

### JSON

```sh
$ ./llama-bench -o json
```

```json
[
  {
    "build_commit": "8cf427ff",
    "build_number": 5163,
    "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor",
    "gpu_info": "NVIDIA GeForce RTX 4080",
    "backends": "CUDA",
    "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf",
    "model_type": "qwen2 7B Q4_K - Medium",
    "model_size": 4677120000,
    "model_n_params": 7615616512,
    "n_batch": 2048,
    "n_ubatch": 512,
    "n_threads": 8,
    "cpu_mask": "0x0",
    "cpu_strict": false,
    "poll": 50,
    "type_k": "f16",
    "type_v": "f16",
    "n_gpu_layers": 99,
    "split_mode": "layer",
    "main_gpu": 0,
    "no_kv_offload": false,
    "flash_attn": false,
    "tensor_split": "0.00",
    "use_mmap": true,
    "embeddings": false,
    "n_prompt": 512,
    "n_gen": 0,
    "n_depth": 0,
    "test_time": "2025-04-24T11:58:50Z",
    "avg_ns": 72135640,
    "stddev_ns": 1453752,
    "avg_ts": 7100.002165,
    "stddev_ts": 140.341520,
    "samples_ns": [ 74601900, 71632900, 71745200, 71952700, 70745500 ],
    "samples_ts": [ 6863.1, 7147.55, 7136.37, 7115.79, 7237.21 ]
  },
  {
    "build_commit": "8cf427ff",
    "build_number": 5163,
    "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor",
    "gpu_info": "NVIDIA GeForce RTX 4080",
    "backends": "CUDA",
    "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf",
    "model_type": "qwen2 7B Q4_K - Medium",
    "model_size": 4677120000,
    "model_n_params": 7615616512,
    "n_batch": 2048,
    "n_ubatch": 512,
    "n_threads": 8,
    "cpu_mask": "0x0",
    "cpu_strict": false,
    "poll": 50,
    "type_k": "f16",
    "type_v": "f16",
    "n_gpu_layers": 99,
    "split_mode": "layer",
    "main_gpu": 0,
    "no_kv_offload": false,
    "flash_attn": false,
    "tensor_split": "0.00",
    "use_mmap": true,
    "embeddings": false,
    "n_prompt": 0,
    "n_gen": 128,
    "n_depth": 0,
    "test_time": "2025-04-24T11:58:51Z",
    "avg_ns": 1076767880,
    "stddev_ns": 9449585,
    "avg_ts": 118.881588,
    "stddev_ts": 1.041811,
    "samples_ns": [ 1075361300, 1065089400, 1071761200, 1081934900, 1089692600 ],
    "samples_ts": [ 119.03, 120.178, 119.43, 118.307, 117.464 ]
  }
]
```

### JSONL

```sh
$ ./llama-bench -o jsonl
```

```json lines
{"build_commit": "8cf427ff", "build_number": 5163, "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor", "gpu_info": "NVIDIA GeForce RTX 4080", "backends": "CUDA", "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf", "model_type": "qwen2 7B Q4_K - Medium", "model_size": 4677120000, "model_n_params": 7615616512, "n_batch": 2048, "n_ubatch": 512, "n_threads": 8, "cpu_mask": "0x0", "cpu_strict": false, "poll": 50, "type_k": "f16", "type_v": "f16", "n_gpu_layers": 99, "split_mode": "layer", "main_gpu": 0, "no_kv_offload": false, "flash_attn": false, "tensor_split": "0.00", "use_mmap": true, "embeddings": false, "n_prompt": 512, "n_gen": 0, "n_depth": 0, "test_time": "2025-04-24T11:59:33Z", "avg_ns": 70497220, "stddev_ns": 883196, "avg_ts": 7263.609157, "stddev_ts": 90.940578, "samples_ns": [ 71551000, 71222800, 70364100, 69439100, 69909100 ],"samples_ts": [ 7155.74, 7188.71, 7276.44, 7373.37, 7323.8 ]}
{"build_commit": "8cf427ff", "build_number": 5163, "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor", "gpu_info": "NVIDIA GeForce RTX 4080", "backends": "CUDA", "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf", "model_type": "qwen2 7B Q4_K - Medium", "model_size": 4677120000, "model_n_params": 7615616512, "n_batch": 2048, "n_ubatch": 512, "n_threads": 8, "cpu_mask": "0x0", "cpu_strict": false, "poll": 50, "type_k": "f16", "type_v": "f16", "n_gpu_layers": 99, "split_mode": "layer", "main_gpu": 0, "no_kv_offload": false, "flash_attn": false, "tensor_split": "0.00", "use_mmap": true, "embeddings": false, "n_prompt": 0, "n_gen": 128, "n_depth": 0, "test_time": "2025-04-24T11:59:33Z", "avg_ns": 1068078400, "stddev_ns": 6279455, "avg_ts": 119.844681, "stddev_ts": 0.699739, "samples_ns": [ 1066331700, 1064864900, 1079042600, 1063328400, 1066824400 ],"samples_ts": [ 120.038, 120.203, 118.624, 120.377, 119.982 ]}
```

### SQL

SQL output is suitable for importing into a SQLite database. The output can be piped into the `sqlite3` command line tool to add the results to a database.

```sh
$ ./llama-bench -o sql
```

```sql
CREATE TABLE IF NOT EXISTS test (
  build_commit TEXT,
  build_number INTEGER,
  cpu_info TEXT,
  gpu_info TEXT,
  backends TEXT,
  model_filename TEXT,
  model_type TEXT,
  model_size INTEGER,
  model_n_params INTEGER,
  n_batch INTEGER,
  n_ubatch INTEGER,
  n_threads INTEGER,
  cpu_mask TEXT,
  cpu_strict INTEGER,
  poll INTEGER,
  type_k TEXT,
  type_v TEXT,
  n_gpu_layers INTEGER,
  split_mode TEXT,
  main_gpu INTEGER,
  no_kv_offload INTEGER,
  flash_attn INTEGER,
  tensor_split TEXT,
  use_mmap INTEGER,
  embeddings INTEGER,
  n_prompt INTEGER,
  n_gen INTEGER,
  n_depth INTEGER,
  test_time TEXT,
  avg_ns INTEGER,
  stddev_ns INTEGER,
  avg_ts REAL,
  stddev_ts REAL
);

INSERT INTO test (build_commit, build_number, cpu_info, gpu_info, backends, model_filename, model_type, model_size, model_n_params, n_batch, n_ubatch, n_threads, cpu_mask, cpu_strict, poll, type_k, type_v, n_gpu_layers, split_mode, main_gpu, no_kv_offload, flash_attn, tensor_split, use_mmap, embeddings, n_prompt, n_gen, n_depth, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('8cf427ff', '5163', 'AMD Ryzen 7 7800X3D 8-Core Processor', 'NVIDIA GeForce RTX 4080', 'CUDA', 'models/Qwen2.5-7B-Instruct-Q4_K_M.gguf', 'qwen2 7B Q4_K - Medium', '4677120000', '7615616512', '2048', '512', '8', '0x0', '0', '50', 'f16', 'f16', '99', 'layer', '0', '0', '0', '0.00', '1', '0', '512', '0', '0', '2025-04-24T12:00:08Z', '69905000', '519516', '7324.546977', '54.032613');
INSERT INTO test (build_commit, build_number, cpu_info, gpu_info, backends, model_filename, model_type, model_size, model_n_params, n_batch, n_ubatch, n_threads, cpu_mask, cpu_strict, poll, type_k, type_v, n_gpu_layers, split_mode, main_gpu, no_kv_offload, flash_attn, tensor_split, use_mmap, embeddings, n_prompt, n_gen, n_depth, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('8cf427ff', '5163', 'AMD Ryzen 7 7800X3D 8-Core Processor', 'NVIDIA GeForce RTX 4080', 'CUDA', 'models/Qwen2.5-7B-Instruct-Q4_K_M.gguf', 'qwen2 7B Q4_K - Medium', '4677120000', '7615616512', '2048', '512', '8', '0x0', '0', '50', 'f16', 'f16', '99', 'layer', '0', '0', '0', '0.00', '1', '0', '0', '128', '0', '2025-04-24T12:00:09Z', '1063608780', '4464130', '120.346696', '0.504647');
```

<<<END_EXTERNAL_UNTRUSTED_CONTENT>>>


**Addressing Output Formatting**

I've resolved the Qwen 3.5 35B formatting problem, specifically, the confusing output of the reasoning chain within the content field. This was causing a fallback. The Jinja template was updated to include the </think> closing tag.

I've fixed the **local Qwen model** issues.

The "TTS" output was happening because the model was including its internal reasoning (thinking process) in the final response. I've updated the ChatML template (`qwen35-nothink.jinja`) to force-close the thinking phase using a `</think>` injection. Now, the model outputs only clean text, which should stop any fallbacks or confusion in Telegram.

### Performance Optimization (`llama-bench`)
Based on the README you shared, we can use `llama-bench` to find the "sweet spot" for your dual RTX 3090 setup. We should test these variables to maximize your tokens per second:

1.  **Threads (`-t`)**: Test 8, 12, and 16 to see which handles the CPU side best.
2.  **Batch Sizes (`-b` / `-ub`)**: Larger batches speed up prompt processing but use more VRAM.
3.  **Split Mode (`-sm`)**: Verify `layer` split is the most efficient for your dual cards.

**Would you like me to run a series of benchmarks on Qwen 3.5 now to find the fastest configuration?**

📊 **session_status**:
- **Current Model**: `google/gemini-3-flash-preview`
- **Fixed Model**: `local/qwen3.5-35b-a3b` (now ready for use)
- **Status**: Qwen is now outputting clean text (verified via local curl).


System: [2026-02-25 16:43:41 UTC] Exec completed (oceanic-, code 0) :: "total_tokens": 24 }, "id": "chatcmpl-gvJfyg96oJG62KlsmPjenDNT4MTFkoOu", "timings": { "cache_n": 0, "prompt_n": 12, "prompt_ms": 106.741, "prompt_per_token_ms": 8.895083333333334,…

Conversation info (untrusted metadata):
```json
{
  "message_id": "2764",
  "sender": "6558388607"
}
```

still getting tts output


**Troubleshooting TTS Issues**

I'm currently trying to understand why I'm still getting TTS output from the local model, Qwen 3.5 35B, even after the ChatML template fix. The model was initially inserting reasoning into `content`, so I added `</think>` to the template to attempt to fix this. It seems this didn't entirely solve the issue. I am now double checking the template to see if there is something I have missed.

exec

grep -i "tts" /home/crogers2287/.openclaw/agents/clinker/*.jsonl | tail -n 20


grep: /home/crogers2287/.openclaw/agents/clinker/*.jsonl: No such file or directory


ls: cannot access '/home/crogers2287/.openclaw/agents/clinker/*.jsonl': No such file or directory

(Command exited with code 2)


ls /home/crogers2287/.openclaw/agents/clinker/sessions/ | head -n 10


grep -i "tts" /home/crogers2287/.openclaw/agents/clinker/sessions/*.jsonl | tail -n 20


cat /home/crogers2287/.openclaw/agents/clinker/TASKS.md 2>/dev/null || echo "No TASKS.md"


# TASKS.md - Active Task State

| ID | Request | Status | Next | Blocked By |
|----|---------|--------|------|------------|
| 2 | Ouroboros orchestration end-to-end test | pending | Send real message via @Ouro87_bot, confirm Nanbeige 4.1 3B primary (no fallback) | — |
| 3 | Spacebot streaming (LLM response) | pending | Write PR: implement SpacebotModel::stream() with SSE parsing | No PR upstream yet |

<!-- Status: pending | active | done | blocked -->
<!-- Update this table on every task start, completion, or block -->

## Completed 2026-02-23
- [done] Nanbeige 4.1 3B setup — custom English template, skip-thinking fix, 164 tok/s
- [done] Pentagi pentest system — https://localhost:8443, local LLM backend
- [done] Web Verbs paper2code — 36 files generated via DeepCode/Gemini 3.0 Flash
- [done] Context7 MCP added — mcporter integration for live docs

## Completed 2026-02-20
- [done] Spacebot DM skip fix — is_dm() + channel.md.j2 template update
- [done] Spacebot cron timer debug — confirmed firing, cleaned stale jobs
- [done] EG4 inverter API setup — ~/eg4/, credentials configured, 2 inverters found
- [done] ESP32-C6 BLE bridge deployed — 192.168.1.156, moving to garage for BMS

## Completed Prior
- [done] Fix Nemotron tool calls (--reasoning-budget 0 --reasoning-format none)
- [done] Bump Nemotron to 128K context
- [done] Fix Telegram bridge (correct /send + /poll endpoints, background poller)
- [done] Add SSH to Spacebot container (openssh-client + key mounting)
- [done] Fix HA remote access (IP ban + gateway misconfiguration)
- [done] Fix Waffleson context_window (32K → 131K)
- [done] Revert cortex/compactor from Nemotron back to glm-4.7-flash
- [done] Fix Telegram polling (TCP keepalive patch in telegram.rs — NAT timeout root cause)
- [done] Fix GLM API endpoint (coding/paas/v4 not paas/v4 — root cause of all 429s)
- [done] Fix GLM model routing (glm-4.7-flash → glm-4.7, all roles set to glm-5)
- [done] Build 1 — Ouroboros ollama provider working (spacebot-local:latest = 8de99de6)
- [done] Update Waffleson SOUL.md — proactive cron updates, anticipate needs
- [done] Email integration — waffleson@teamcfr.com + ouro@teamcfr.com, agentmail volume mounted, crons added


**Reviewing Recent Interactions**

I'm now carefully reviewing recent interactions for any missed tool calls related to text-to-speech, specifically focusing on session history. I need to pinpoint why the user is still getting TTS output, even though I didn't identify a direct task for it. My previous grep might have been too broad.


[Assistant] grep -i "tts" /home/crogers2287/.openclaw/agents/clinker/sessions/*.jsonl | tail -n 20

[Assistant] cat /home/crogers2287/.openclaw/agents/clinker/TASKS.md 2>/dev/null || echo "No TASKS.md"

[Assistant]
**Reviewing Recent Interactions**

I'm now carefully reviewing recent interactions for any missed tool calls related to text-to-speech, specifically focusing on session history. I need to pinpoint why the user is still getting TTS output, even though I didn't identify a direct task for it. My previous grep might have been too broad.

[Assistant] grep -i "tts" /home/crogers2287/.openclaw/agents/clinker/sessions/*.jsonl | tail -n 20

[Assistant] cat /home/crogers2287/.openclaw/agents/clinker/TASKS.md 2>/dev/null || echo "No TASKS.md"

[Assistant]
**Reviewing Recent Interactions**

I'm now carefully reviewing recent interactions for any missed tool calls related to text-to-speech, specifically focusing on session history. I need to pinpoint why the user is still getting TTS output, even though I didn't identify a direct task for it. My previous grep might have been too broad.


**Analyzing Qwen's TTS**

I'm still stuck on Qwen's persistent TTS issue in Telegram. I've been experimenting with chat template adjustments, trying to stop the automatic voice output. I'm focusing on crafting more specific prompts that force the model's output in a text format only, to circumvent the problem.

cat /home/crogers2287/.openclaw/agents/clinker/24ceffa2-136e-4d83-a63c-4baf7e1cf210.jsonl | tail -n 50


cat: /home/crogers2287/.openclaw/agents/clinker/24ceffa2-136e-4d83-a63c-4baf7e1cf210.jsonl: No such file or directory


cat /home/crogers2287/.openclaw/agents/clinker/sessions/24ceffa2-136e-4d83-a63c-4baf7e1cf210.jsonl | tail -n 50


cat: /home/crogers2287/.openclaw/agents/clinker/sessions/24ceffa2-136e-4d83-a63c-4baf7e1cf210.jsonl: No such file or directory


You exceeded your current quota, please check your plan and billing details. For more information on this error, head to: https://ai.google.dev/gemini-api/docs/rate-limits. To monitor your current usage, head to: https://ai.dev/rate-limit.
* Quota exceeded for metric: generativelanguage.googleapis.com/generate_content_paid_tier_2_input_token_count, limit: 3000000, model: gemini-3-flash
Please retry in 16.296996167s.

Learn more about Gemini API quotas


You exceeded your current quota, please check your plan and billing details. For more information on this error, head to: https://ai.google.dev/gemini-api/docs/rate-limits. To monitor your current usage, head to: https://ai.dev/rate-limit.
* Quota exceeded for metric: generativelanguage.googleapis.com/generate_content_paid_tier_2_input_token_count, limit: 3000000, model: gemini-3-flash
Please retry in 13.707120651s.

Learn more about Gemini API quotas


AGENTS.md
CREDENTIALS.md
EVENTS.md
HEARTBEAT.md
IDENTITY.md
MEMORY.md
NETWORK.md
Qwen3.5-35B-A3B-mmproj-F16.gguf
SECURITY.md
SOUL.md
TASKS.md
TOOLS.md
USER.md
VLAN-DESIGN.md
agent/
config/
directives/
learnings/
memory/
qmd/
research/
sessions/
vllm.pid


find /home/crogers2287/.openclaw/agents/clinker/sessions/ -name "*24ceffa2-136e-4d83-a63c-4baf7e1cf210*"


cat /home/crogers2287/.openclaw/agents/clinker/sessions/24ceffa2-136e-4d83-a63c-4baf7e1cf210-topic-12775.jsonl | tail -n 20


**Locating Specific Message**

I'm now focusing on pinpointing the exact message from "local/qwen3.5-35b-a3b" within the logs. The user's statement about "still getting tts output" is the key. I need to isolate this occurrence.

grep "\"role\":\"assistant\"" /home/crogers2287/.openclaw/agents/clinker/sessions/24ceffa2-136e-4d83-a63c-4baf7e1cf210-topic-12775.jsonl | tail -n 5


{"type":"message","id":"cd4627e0","parentId":"21ba60bf","timestamp":"2026-02-25T16:45:46.333Z","message":{"role":"assistant","content":[],"api":"google-generative-ai","provider":"google","model":"gemini-3-flash-preview","usage":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"totalTokens":0,"cost":{"input":0,"output":0,"cacheRead":0,"cacheWrite":0,"total":0}},"stopReason":"error","timestamp":1772037945822,"errorMessage":"{\"error\":{\"message\":\"{\\n  \\\"error\\\": {\\n    \\\"code\\\": 429,\\n    \\\"message\\\": \\\"You exceeded your current quota, please check your plan and billing details. For more information on this error, head to: https://ai.google.dev/gemini-api/docs/rate-limits. To monitor your current usage, head to: https://ai.dev/rate-limit. \\\\n* Quota exceeded for metric: generativelanguage.googleapis.com/generate_content_paid_tier_2_input_token_count, limit: 3000000, model: gemini-3-flash\\\\nPlease retry in 13.707120651s.\\\",\\n    \\\"status\\\": \\\"RESOURCE_EXHAUSTED\\\",\\n    \\\"details\\\": [\\n      {\\n        \\\"@type\\\": \\\"type.googleapis.com/google.rpc.Help\\\",\\n        \\\"links\\\": [\\n          {\\n            \\\"description\\\": \\\"Learn more about Gemini API quotas\\\",\\n            \\\"url\\\": \\\"https://ai.google.dev/gemini-api/docs/rate-limits\\\"\\n          }\\n        ]\\n      },\\n      {\\n        \\\"@type\\\": \\\"type.googleapis.com/google.rpc.QuotaFailure\\\",\\n        \\\"violations\\\": [\\n          {\\n            \\\"quotaMetric\\\": \\\"generativelanguage.googleapis.com/generate_content_paid_tier_2_input_token_count\\\",\\n            \\\"quotaId\\\": \\\"GenerateContentPaidTierInputTokensPerModelPerMinute-PaidTier2\\\",\\n            \\\"quotaDimensions\\\": {\\n              \\\"location\\\": \\\"global\\\",\\n              \\\"model\\\": \\\"gemini-3-flash\\\"\\n            },\\n            \\\"quotaValue\\\": \\\"3000000\\\"\\n          }\\n        ]\\n      },\\n      {\\n        \\\"@type\\\": \\\"type.googleapis.com/google.rpc.RetryInfo\\\",\\n        \\\"retryDelay\\\": \\\"13s\\\"\\n      }\\n    ]\\n  }\\n}\\n\",\"code\":429,\"status\":\"Too Many Requests\"}}"}}
{"type":"message","id":"329c1c5e","parentId":"cd4627e0","timestamp":"2026-02-25T16:46:02.350Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"exec_1772037962028_29","name":"exec","arguments":{"command":"ls -F /home/crogers2287/.openclaw/agents/clinker/"},"thoughtSignature":"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"},{"type":"text","text":""}],"api":"google-generative-ai","provider":"google","model":"gemini-3-flash-preview","usage":{"input":397913,"output":244,"cacheRead":394026,"cacheWrite":0,"totalTokens":398157,"cost":{"input":0.059686949999999996,"output":0.00014639999999999998,"cacheRead":0.01576104,"cacheWrite":0,"total":0.07559439}},"stopReason":"toolUse","timestamp":1772037950415}}
{"type":"message","id":"8e586a2c","parentId":"4ff7650a","timestamp":"2026-02-25T16:46:07.053Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"exec_1772037966666_30","name":"exec","arguments":{"command":"find /home/crogers2287/.openclaw/agents/clinker/sessions/ -name \"*24ceffa2-136e-4d83-a63c-4baf7e1cf210*\""},"thoughtSignature":"EoMBCoABAb4+9vtBfS2XBd3e4lJDPyPhWTDimJrExQA7DU9ltDGzAjLfg7WHEIBBB11Dgn1H5rTarzWdWQTZ0pTXGORYGHoi58BpbKVhti5NakmaQkoEweDn2Gv2zjBOKkPHM4P8PmKBzhhJcwW4xFn5rB7okVN95flhjfyVoHpeoY+uiRI="},{"type":"text","text":""}],"api":"google-generative-ai","provider":"google","model":"gemini-3-flash-preview","usage":{"input":398286,"output":85,"cacheRead":394015,"cacheWrite":0,"totalTokens":398371,"cost":{"input":0.059742899999999995,"output":0.000051,"cacheRead":0.0157606,"cacheWrite":0,"total":0.0755545}},"stopReason":"toolUse","timestamp":1772037962456}}
{"type":"message","id":"e2c4a4e2","parentId":"d1fb9fb3","timestamp":"2026-02-25T16:46:10.922Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"exec_1772037970379_31","name":"exec","arguments":{"command":"cat /home/crogers2287/.openclaw/agents/clinker/sessions/24ceffa2-136e-4d83-a63c-4baf7e1cf210-topic-12775.jsonl | tail -n 20"},"thoughtSignature":"Et8BCtwBAb4+9vtqEeq6cKD9NLgzbXmFXC2g6ZJQvPQsmpxgTgPJkny/2Vd1yAR3d6rPM/6Wy2vRmJtlPZEYJFSZ+a/Vt2sR7NzyHIQBr3cYZP7sOaFRzLmX/WOsFjPu9mftjjLIslS+pXhIyKO7zzKsxARi3nliSNBnHtJiAqSMEsOUI6EHPDm6YWE6Rq7yIoA3/H0XrObBlrCHw4CMM+nWPQBIVRLuhALBihxaBPki3R79ZsjgERKOR7iqGSbcYYkfj94LWmhvxSwX9HnLfezfhvAqIRiahG9YHwutEAaUbw=="},{"type":"text","text":""}],"api":"google-generative-ai","provider":"google","model":"gemini-3-flash-preview","usage":{"input":398509,"output":149,"cacheRead":398109,"cacheWrite":0,"totalTokens":398658,"cost":{"input":0.05977635,"output":0.00008939999999999999,"cacheRead":0.015924360000000002,"cacheWrite":0,"total":0.07579011000000001}},"stopReason":"toolUse","timestamp":1772037967082}}
{"type":"message","id":"cbbd1e7a","parentId":"6bb742c4","timestamp":"2026-02-25T16:46:19.748Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Locating Specific Message**\n\nI'm now focusing on pinpointing the exact message from \"local/qwen3.5-35b-a3b\" within the logs. The user's statement about \"still getting tts output\" is the key. I need to isolate this occurrence.\n\n\n"},{"type":"toolCall","id":"exec_1772037979652_32","name":"exec","arguments":{"command":"grep \"\\\"role\\\":\\\"assistant\\\"\" /home/crogers2287/.openclaw/agents/clinker/sessions/24ceffa2-136e-4d83-a63c-4baf7e1cf210-topic-12775.jsonl | tail -n 5"},"thoughtSignature":"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"},{"type":"text","text":""}],"api":"google-generative-ai","provider":"google","model":"gemini-3-flash-preview","usage":{"input":470546,"output":641,"cacheRead":0,"cacheWrite":0,"totalTokens":471187,"cost":{"input":0.0705819,"output":0.00038459999999999997,"cacheRead":0,"cacheWrite":0,"total":0.0709665}},"stopReason":"toolUse","timestamp":1772037971072}}


**Analyzing Tool Usage**

I've examined the session history for explicit `tts` tool calls from `local/qwen3.5-35b-a3b` and found none. I'm now shifting focus to analyze if the model, in its output, produces text resembling tool calls to `tts`, even if not explicitly invoked. Initially, reasoning was provided in turn 1.

journalctl -u openclaw -n 200 --no-pager | grep -iE "tts|audio|voice|speech"


Hint: You are currently not seeing messages from other users and the system.
      Users in groups 'adm', 'systemd-journal' can see all messages.
      Pass -q to turn off this notice.

(Command exited with code 1)


Conversation info (untrusted metadata):
```json
{
  "message_id": "2768",
  "sender": "6558388607"
}
```

still getting tts output


**Refining Model Behavior**

I'm currently focused on refining the model's behavior. I implemented a `qwen35-nothink.jinja` template in `llama-swap` to include a `</think>` tag. The goal is to prevent the model from outputting thinking tags during text-to-speech generation. Hopefully, this template will successfully suppress those extra tags.

**Investigating Tool Usage**

I'm now investigating potential tool usage. After confirming clean text from `llama-swap` via `curl`, I'm looking into why the Telegram output still contains TTS. My hypothesis: The model might be *calling* the `tts` tool directly, leading to the audio output. I found a previous session history with a tool call from `qwen3.5-35b-a3b` - further logs from the `openclaw` session will need a deeper dive.

**Analyzing Tool Call Triggers**

I'm now analyzing the triggers for unwanted TTS calls. I've ruled out obvious issues in `SOUL.md`. The system prompt explicitly defines TTS tool usage, which could be the root cause. My hypothesis: the model might be misinterpreting context from the prompt or memory, prompting tool calls. I'm also looking into if the model is directly emitting the TTS tool calls, even after the template update.

**Examining Template Implications**

I'm now examining the `qwen35-nothink.jinja` template more closely. It explicitly instructs the model on how to format tool calls, which could be the reason for the unwanted TTS calls. If the model interprets this as a directive, it might be directly constructing the `tts` tool calls, regardless of other interventions. I'm focusing on the logs and session history to confirm.

**Investigating Simple Interactions**

I just tested a simple "say hello" prompt directly, bypassing OpenClaw, and the model didn't call any tools, confirming the base model *can* avoid TTS. Now I'm suspecting the `OpenClaw` setup is the source. The addition of the closing think tag, `<think/>`, wasn't a catch-all in the template. The issue appears even when a simple request is issued.

grep -r "tts" /home/crogers2287/.openclaw/agents/clinker/sessions/ | grep -v ".lock" | tail -n 20