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.
Feb 25 16:37:53 fred llama-swap[1073114]: srv  log_server_r: done request: HEAD / 127.0.0.1 200
Feb 25 16:37:53 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:37:53 fred llama-swap[1073114]: [INFO] <qwen3.5-35b-a3b> recovered from client disconnection during streaming
Feb 25 16:37:53 fred llama-swap[1073114]: [WARN] error processing streaming response: no valid JSON data found in stream, path=/v1/chat/completions
Feb 25 16:37:53 fred llama-swap[1073114]: [INFO] Request 127.0.0.1 "POST /v1/chat/completions HTTP/1.1" 200 13347 "OpenAI/JS 6.10.0" 8.824880114s
Feb 25 16:37:53 fred llama-swap[1073114]: srv          stop: cancel task, id_task = 5590
Feb 25 16:37:53 fred llama-swap[1073114]: slot      release: id  3 | task 5590 | stop processing: n_tokens = 23266, truncated = 0
Feb 25 16:37:53 fred llama-swap[1073114]: srv  update_slots: all slots are idle
Feb 25 16:38:11 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 51.931µs
Feb 25 16:38:11 fred llama-swap[1073114]: [INFO] Request ::1 "HEAD / HTTP/1.1" 404 0 "curl/8.14.1" 4.074µs
Feb 25 16:38:11 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 49.329µs
Feb 25 16:38:20 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:38:23 fred llama-swap[1073114]: srv  log_server_r: done request:   127.0.0.1 400
Feb 25 16:38:23 fred llama-swap[1073114]: srv  log_server_r: done request: HEAD / 127.0.0.1 200
Feb 25 16:38:23 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:38:42 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 87.364µs
Feb 25 16:38:42 fred llama-swap[1073114]: [INFO] Request ::1 "HEAD / HTTP/1.1" 404 0 "curl/8.14.1" 8.63µs
Feb 25 16:38:42 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 41.72µs
Feb 25 16:38:50 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:38:53 fred llama-swap[1073114]: srv  log_server_r: done request:   127.0.0.1 400
Feb 25 16:38:53 fred llama-swap[1073114]: srv  log_server_r: done request: HEAD / 127.0.0.1 200
Feb 25 16:38:53 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:39:10 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 48.529µs
Feb 25 16:39:10 fred llama-swap[1073114]: [INFO] Request ::1 "HEAD / HTTP/1.1" 404 0 "curl/8.14.1" 6.685µs
Feb 25 16:39:10 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 55.701µs
Feb 25 16:39:19 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:39:22 fred llama-swap[1073114]: srv  log_server_r: done request:   127.0.0.1 400
Feb 25 16:39:22 fred llama-swap[1073114]: srv  log_server_r: done request: HEAD / 127.0.0.1 200
Feb 25 16:39:22 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:39:40 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 39.066µs
Feb 25 16:39:40 fred llama-swap[1073114]: [INFO] Request ::1 "HEAD / HTTP/1.1" 404 0 "curl/8.14.1" 9.822µs
Feb 25 16:39:40 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 46.723µs
Feb 25 16:39:49 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:39:52 fred llama-swap[1073114]: srv  log_server_r: done request:   127.0.0.1 400
Feb 25 16:39:52 fred llama-swap[1073114]: srv  log_server_r: done request: HEAD / 127.0.0.1 200
Feb 25 16:39:52 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:40:09 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 96.277µs
Feb 25 16:40:09 fred llama-swap[1073114]: [INFO] Request ::1 "HEAD / HTTP/1.1" 404 0 "curl/8.14.1" 4.947µs
Feb 25 16:40:09 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 36.136µs
Feb 25 16:40:19 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:40:22 fred llama-swap[1073114]: srv  log_server_r: done request:   127.0.0.1 400
Feb 25 16:40:22 fred llama-swap[1073114]: srv  log_server_r: done request: HEAD / 127.0.0.1 200
Feb 25 16:40:23 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:40:39 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 49.36µs
Feb 25 16:40:39 fred llama-swap[1073114]: [INFO] Request ::1 "HEAD / HTTP/1.1" 404 0 "curl/8.14.1" 4.957µs
Feb 25 16:40:39 fred llama-swap[1073114]: [INFO] Request ::1 "GET / HTTP/1.1" 302 26 "curl/8.14.1" 26.727µs
Feb 25 16:40:49 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:40:52 fred llama-swap[1073114]: srv  log_server_r: done request:   127.0.0.1 400
Feb 25 16:40:52 fred llama-swap[1073114]: srv  log_server_r: done request: HEAD / 127.0.0.1 200
Feb 25 16:40:52 fred llama-swap[1073114]: srv  log_server_r: done request: GET / 127.0.0.1 200


**Analyzing Streaming Errors**

I've been digging into those logs, and it looks like we're still having issues with streaming. The client disconnection during streaming, paired with the "no valid JSON data" error, points to two major areas: model outputting malformed data or some hiccup in the stream itself. I suspect either the model's output isn't consistently valid JSON, or there are interruptions in the data transmission.

exec

cat /home/crogers2287/.openclaw/agents/clinker/config.yaml 2>/dev/null || echo "No agent config.yaml"


No agent config.yaml


error: missing required argument 'path'

(Command exited with code 1)


Claude Opus 4.6

Input: text image

Claude Sonnet 4.6

Input: text image

Claude Sonnet 4.5

Input: text image

Claude Haiku 4.5

Input: text image

Gemini 3 Flash

Input: text image

GLM 4.7

Input: text

Input: text

Input: text

GLM-4.7 Flash

Input: text

GLM-4.7 FlashX

Input: text

MiniMax M2.1

Input: text

MiniMax M2.1 Lightning

Input: text

MiniMax M2.5

Input: text

MiniMax M2.5 Lightning

Input: text

Claude Opus 4.6 (Copilot)

Input: text image

Claude Opus 4.6 Fast (Copilot)

Input: text image

Claude Opus 4.5 (Copilot)

Input: text image

Claude Sonnet 4.6 (Copilot)

Input: text image

Claude Sonnet 4.5 (Copilot)

Input: text image

Claude Sonnet 4 (Copilot)

Input: text image

Claude Haiku 4.5 (Copilot)

Input: text image

GPT-5 (Copilot)

Input: text image

GPT-4o (Copilot)

Input: text image

Gemini 3 Pro (Copilot)

Input: text image

Gemini 3 Flash (Copilot)

Input: text image

Qwen3.5 35B-A3B (200K, Vision)

Input: text image

Qwen3.5 27B

Input: text

Qwen3.5 27B Vision

Input: text image

Nemotron 3 Nano 30B

Input: text

GLM 4.7 Flash

Input: text

Nanbeige 4.1 3B (Reasoner)

Input: text

Prompt: Write key decisions, state changes, blockers, and artifacts to memory/YYYY-MM-DD.md. Format: ## [HH:MM] Topic. Update TASKS.md status if any tasks changed. Reply NO_REPLY if nothing worth storing.

Session nearing compaction. Extract only durable state: decisions, blockers, artifacts, state changes. No fluff.

grant maya magoo ghc-marketing cannadeals-marketing cardone clinker mark-twain carl selena

memory-core telegram minimax-portal-auth email

Claude Opus 4.6

Input: text image

Claude Sonnet 4.6

Input: text image

Claude Sonnet 4.5

Input: text image

Claude Haiku 4.5

Input: text image

Gemini 3 Flash

Input: text image

GLM 4.7

Input: text

Input: text

Input: text

GLM-4.7 Flash

Input: text

GLM-4.7 FlashX

Input: text

MiniMax M2.1

Input: text

MiniMax M2.1 Lightning

Input: text

MiniMax M2.5

Input: text

MiniMax M2.5 Lightning

Input: text

Claude Opus 4.6 (Copilot)

Input: text image

Claude Opus 4.6 Fast (Copilot)

Input: text image

Claude Opus 4.5 (Copilot)

Input: text image

Claude Sonnet 4.6 (Copilot)

Input: text image

Claude Sonnet 4.5 (Copilot)

Input: text image

Claude Sonnet 4 (Copilot)

Input: text image

Claude Haiku 4.5 (Copilot)

Input: text image

GPT-5 (Copilot)

Input: text image

GPT-4o (Copilot)

Input: text image

Gemini 3 Pro (Copilot)

Input: text image

Gemini 3 Flash (Copilot)

Input: text image

Qwen3.5 35B-A3B (200K, Vision)

Input: text image

Qwen3.5 27B

Input: text

Qwen3.5 27B Vision

Input: text image

Nemotron 3 Nano 30B

Input: text

GLM 4.7 Flash

Input: text

Nanbeige 4.1 3B (Reasoner)

Input: text

Prompt: Write key decisions, state changes, blockers, and artifacts to memory/YYYY-MM-DD.md. Format: ## [HH:MM] Topic. Update TASKS.md status if any tasks changed. Reply NO_REPLY if nothing worth storing.

Claude Opus 4.6

Input: text image

Claude Sonnet 4.6

Input: text image

Claude Sonnet 4.5

Input: text image

Claude Haiku 4.5

Input: text image

Gemini 3 Flash

Input: text image

GLM 4.7

Input: text

Input: text

Input: text

GLM-4.7 Flash

Input: text

GLM-4.7 FlashX

Input: text

MiniMax M2.1

Input: text

MiniMax M2.1 Lightning

Input: text

MiniMax M2.5

Input: text

MiniMax M2.5 Lightning

Input: text

Claude Opus 4.6 (Copilot)

Input: text image

Claude Opus 4.6 Fast (Copilot)

Input: text image

Claude Opus 4.5 (Copilot)

Input: text image

Claude Sonnet 4.6 (Copilot)

Input: text image

Claude Sonnet 4.5 (Copilot)

Input: text image

Claude Sonnet 4 (Copilot)

Input: text image

Claude Haiku 4.5 (Copilot)

Input: text image

GPT-5 (Copilot)

Input: text image

GPT-4o (Copilot)

Input: text image

Gemini 3 Pro (Copilot)

Input: text image

Gemini 3 Flash (Copilot)

Input: text image

Qwen3.5 35B-A3B (200K, Vision)

Input: text image

Qwen3.5 27B

Input: text

Qwen3.5 27B Vision

Input: text image

Nemotron 3 Nano 30B

Input: text

GLM 4.7 Flash

Input: text

Nanbeige 4.1 3B (Reasoner)

Input: text

Prompt: Write key decisions, state changes, blockers, and artifacts to memory/YYYY-MM-DD.md. Format: ## [HH:MM] Topic. Update TASKS.md status if any tasks changed. Reply NO_REPLY if nothing worth storing.

Claude Opus 4.6

Input: text image

Claude Sonnet 4.6

Input: text image

Claude Sonnet 4.5

Input: text image

Claude Haiku 4.5

Input: text image

Gemini 3 Flash

Input: text image

GLM 4.7

Input: text

Input: text

Input: text

GLM-4.7 Flash

Input: text

GLM-4.7 FlashX

Input: text

MiniMax M2.1

Input: text

MiniMax M2.1 Lightning

Input: text

MiniMax M2.5

Input: text

MiniMax M2.5 Lightning

Input: text

Claude Opus 4.6 (Copilot)

Input: text image

Claude Opus 4.6 Fast (Copilot)

Input: text image

Claude Opus 4.5 (Copilot)

Input: text image

Claude Sonnet 4.6 (Copilot)

Input: text image

Claude Sonnet 4.5 (Copilot)

Input: text image

Claude Sonnet 4 (Copilot)

Input: text image

Claude Haiku 4.5 (Copilot)

Input: text image

GPT-5 (Copilot)

Input: text image

GPT-4o (Copilot)

Input: text image

Gemini 3 Pro (Copilot)

Input: text image

Gemini 3 Flash (Copilot)

Input: text image

Qwen3.5 35B-A3B (200K, Vision)

Input: text image

Qwen3.5 27B

Input: text

Qwen3.5 27B Vision

Input: text image

Nemotron 3 Nano 30B

Input: text

GLM 4.7 Flash

Input: text

Nanbeige 4.1 3B (Reasoner)

Input: text

Prompt: Write key decisions, state changes, blockers, and artifacts to memory/YYYY-MM-DD.md. Format: ## [HH:MM] Topic. Update TASKS.md status if any tasks changed. Reply NO_REPLY if nothing worth storing.

Claude Opus 4.6

Input: text image

Claude Sonnet 4.6

Input: text image

Claude Sonnet 4.5

Input: text image

Claude Haiku 4.5

Input: text image

Gemini 3 Flash

Input: text image

GLM 4.7

Input: text

Input: text

Input: text

GLM-4.7 Flash

Input: text

GLM-4.7 FlashX

Input: text

MiniMax M2.1

Input: text

MiniMax M2.1 Lightning

Input: text

MiniMax M2.5

Input: text

MiniMax M2.5 Lightning

Input: text

Claude Opus 4.6 (Copilot)

Input: text image

Claude Opus 4.6 Fast (Copilot)

Input: text image

Claude Opus 4.5 (Copilot)

Input: text image

Claude Sonnet 4.6 (Copilot)

Input: text image

Claude Sonnet 4.5 (Copilot)

Input: text image

Claude Sonnet 4 (Copilot)

Input: text image

Claude Haiku 4.5 (Copilot)

Input: text image

GPT-5 (Copilot)

Input: text image

GPT-4o (Copilot)

Input: text image

Gemini 3 Pro (Copilot)

Input: text image

Gemini 3 Flash (Copilot)

Input: text image

Qwen3.5 35B-A3B (200K, Vision)

Input: text image

Qwen3.5 27B

Input: text

Qwen3.5 27B Vision

Input: text image

Nemotron 3 Nano 30B

Input: text

GLM 4.7 Flash

Input: text

Nanbeige 4.1 3B (Reasoner)

Input: text

Prompt: Write key decisions, state changes, blockers, and artifacts to memory/YYYY-MM-DD.md. Format: ## [HH:MM] Topic. Update TASKS.md status if any tasks changed. Reply NO_REPLY if nothing worth storing.

Claude Opus 4.6

Input: text image

Claude Sonnet 4.6

Input: text image

Claude Sonnet 4.5

Input: text image

Claude Haiku 4.5

Input: text image

Gemini 3 Flash

Input: text image

GLM 4.7

Input: text

Input: text

Input: text

GLM-4.7 Flash

Input: text

GLM-4.7 FlashX

Input: text

MiniMax M2.1

Input: text

MiniMax M2.1 Lightning

Input: text

MiniMax M2.5

Input: text

MiniMax M2.5 Lightning

Input: text

Claude Opus 4.6 (Copilot)

Input: text image

Claude Opus 4.6 Fast (Copilot)

Input: text image

Claude Opus 4.5 (Copilot)

Input: text image

Claude Sonnet 4.6 (Copilot)

Input: text image

Claude Sonnet 4.5 (Copilot)

Input: text image

Claude Sonnet 4 (Copilot)

Input: text image

Claude Haiku 4.5 (Copilot)

Input: text image

GPT-5 (Copilot)

Input: text image

GPT-4o (Copilot)

Input: text image

Gemini 3 Pro (Copilot)

Input: text image

Gemini 3 Flash (Copilot)

Input: text image

Qwen3.5 35B-A3B (200K, Vision)

Input: text image

Qwen3.5 27B

Input: text

Qwen3.5 27B Vision

Input: text image

Nemotron 3 Nano 30B

Input: text

GLM 4.7 Flash

Input: text

Nanbeige 4.1 3B (Reasoner)

Input: text

Prompt: Write key decisions, state changes, blockers, and artifacts to memory/YYYY-MM-DD.md. Format: ## [HH:MM] Topic. Update TASKS.md status if any tasks changed. Reply NO_REPLY if nothing worth storing.

Claude Opus 4.6

Input: text image

Claude Sonnet 4.6

Input: text image

Claude Sonnet 4.5

Input: text image

Claude Haiku 4.5

Input: text image

Gemini 3 Flash

Input: text image

GLM 4.7

Input: text

Input: text

Input: text

GLM-4.7 Flash

Input: text

GLM-4.7 FlashX

Input: text

MiniMax M2.1

Input: text

MiniMax M2.1 Lightning

Input: text

MiniMax M2.5

Input: text

MiniMax M2.5 Lightning

Input: text

Claude Opus 4.6 (Copilot)

Input: text image

Claude Opus 4.6 Fast (Copilot)

Input: text image

Claude Opus 4.5 (Copilot)

Input: text image

Claude Sonnet 4.6 (Copilot)

Input: text image

Claude Sonnet 4.5 (Copilot)

Input: text image

Claude Sonnet 4 (Copilot)

Input: text image

Claude Haiku 4.5 (Copilot)

Input: text image

GPT-5 (Copilot)

Input: text image

GPT-4o (Copilot)

Input: text image

Gemini 3 Pro (Copilot)

Input: text image

Gemini 3 Flash (Copilot)

Input: text image

Qwen3.5 35B-A3B (200K, Vision)

Input: text image

Qwen3.5 27B

Input: text

Qwen3.5 27B Vision

Input: text image

Nemotron 3 Nano 30B

Input: text

GLM 4.7 Flash

Input: text

Nanbeige 4.1 3B (Reasoner)

Input: text

Prompt: Write key decisions, state changes, blockers, and artifacts to memory/YYYY-MM-DD.md. Format: ## [HH:MM] Topic. Update TASKS.md status if any tasks changed. Reply NO_REPLY if nothing worth storing.

Claude Opus 4.6

Input: text image

Claude Sonnet 4.6

Input: text image

Claude Sonnet 4.5

Input: text image

Claude Haiku 4.5

Input: text image

Gemini 3 Flash

Input: text image

GLM 4.7

Input: text

Input: text

Input: text

GLM-4.7 Flash

Input: text

GLM-4.7 FlashX

Input: text

MiniMax M2.1

Input: text

MiniMax M2.1 Lightning

Input: text

MiniMax M2.5

Input: text

MiniMax M2.5 Lightning

Input: text

Claude Opus 4.6 (Copilot)

Input: text image

Claude Opus 4.6 Fast (Copilot)

Input: text image

Claude Opus 4.5 (Copilot)

Input: text image

Claude Sonnet 4.6 (Copilot)

Input: text image

Claude Sonnet 4.5 (Copilot)

Input: text image

Claude Sonnet 4 (Copilot)

Input: text image

Claude Haiku 4.5 (Copilot)

Input: text image

GPT-5 (Copilot)

Input: text image

GPT-4o (Copilot)

Input: text image

Gemini 3 Pro (Copilot)

Input: text image

Gemini 3 Flash (Copilot)

Input: text image

Qwen3.5 35B-A3B (200K, Vision)

Input: text image

Qwen3.5 27B

Input: text

Qwen3.5 27B Vision

Input: text image

Nemotron 3 Nano 30B

Input: text

GLM 4.7 Flash

Input: text

Nanbeige 4.1 3B (Reasoner)

Input: text

Prompt: Write key decisions, state changes, blockers, and artifacts to memory/YYYY-MM-DD.md. Format: ## [HH:MM] Topic. Update TASKS.md status if any tasks changed. Reply NO_REPLY if nothing worth storing.


**Investigating Config Options**

I've examined the configuration and haven't found any global Text-to-Speech setting overriding local model behavior. Now, I'm focusing on the model list, specifically the provided example, to see if there are any hints related to Text-to-Speech in that section. It's a key spot to probe.

**Analyzing Template Impact**

I've discovered the `nanbeige` alias is associated with a specific custom template, `qwen35-nothink.jinja`, in the `llama-swap` config. I'm now investigating whether this template is correctly stripping thinking tags, as this could be the root cause of the persistent Text-to-Speech output. My next step involves inspecting the server response to check for the presence of these tags and the implications for OpenClaw's interpretation.


[Assistant] ✅ New session started · model: local/qwen3.5-35b-a3b

[User]
System: [2026-02-25 15:50:20 UTC] ⚠ Post-Compaction Audit: The following required startup files were not read after context reset:
  - WORKFLOW_AUTO.md
  - memory\/\d{4}-\d{2}-\d{2}\.md

Please read them now using the Read tool before continuing. This ensures your operating protocols are restored after memory compaction.

A new session was started via /new or /reset. Greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning.

[Assistant] Question: 2026-02-25

[User]
System: [2026-02-25 15:57:55 UTC] Model switched to GLM (zai/glm-5).

Conversation info (untrusted metadata):
```json
{
  "message_id": "2729",
  "sender": "6558388607"
}
```

why does the local model keep responding with only tts?

[Assistant]
The user is asking why the local model keeps responding with only TTS (text-to-speech). This is a technical question about model behavior. Let me check memory for any relevant context about this issue.

Question: local model TTS responding text to speech nanbeige qwen

[Assistant]
The memory search didn't directly reveal the TTS issue. Let me search more specifically for this problem.

Question: TTS only response empty content tool call NO_REPLY

[Assistant]
Still not finding the specific TTS issue. Let me check MEMORY.md for more context on the model behavior issue.

Question: reasoning_content output content assistant generation template

[Assistant] I found a reference in MEMORY.md about Nanbeige outputting to `reasoning_content` instead of `content`. Let me look at that section more closely since it might be related.

[Assistant]
Let me search for the Nanbeige fix section more specifically.

Question: nanbeige fix reasoning content template jinja nothink

[Assistant]
Okay, I found the relevant info. The Nanbeige fix is about `reasoning_content` vs `content`, not TTS. But the user is asking specifically about the local model responding with only TTS.

Let me check the agent config or look at recent issues. The user says the local model keeps responding with "only tts" - this could mean:
1. The model is calling the TTS tool instead of responding with text
2. The model outputs empty content but triggers TTS somehow

Let me check if there's something in the config or recent sessions about this.

Found it. The issue is **Qwen 3.5 35B** — it's a reasoning model that outputs to `reasoning_content` instead of `content`, so OpenClaw sees an empty response and may be defaulting to TTS or some other fallback behavior.

This is the same class of bug we fixed with Nanbeige (see MEMORY.md lines 198-210).

**Quick fix options:**

1. **Apply a similar chat template** for Qwen 3.5 to force it to output to `content` instead of `reasoning_content`

2. **Switch default model** to something that doesn't have this reasoning output split (GLM-5, Nemotron, or a non-reasoning variant)

Which would you prefer? If you want to keep using Qwen locally, I'll need to create a jinja template like `/home/crogers2287/llama-swap/qwen-nothink.jinja` to fix the output routing.

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2731",
  "sender": "6558388607"
}
```

To disable thinking / reasoning, use --chat-template-kwargs "{\"enable_thinking\": false}" Qwen3.5-35B-A3B For this guide we will be utilizing Dynamic 4-bit which works great on a 24GB RAM / Mac device for fast inference. Because the model is only around 72GB at full F16 precision, we won't need to worry much about performance. GGUF: Qwen3.5-35B-A3B-GGUF ✨ Run in llama.cpp Obtain the latest llama.cpp on GitHub here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. Copy apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggml-org/llama.cpp cmake llama.cpp -B llama.cpp/build \ -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split cp llama.cpp/build/bin/llama-* llama.cpp If you want to use llama.cpp directly to load models, you can do the below: (:Q3_K_XL) is the quantization type. You can also download via Hugging Face (point 3). This is similar to ollama run . Use export LLAMA_CACHE="folder" to force llama.cpp to save to a specific location. The model has a maximum of 256K context length. Follow one of the specific commands below, according to your use-case: Thinking mode: Precise coding tasks (e.g. WebDev): Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 Non-thinking mode: General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.7 \ --top-p 0.8 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Reasoning tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Download the model via (after installing pip install huggingface_hub hf_transfer ). You can choose UD-Q4_K_XL (dynamic 4bit) or other quantized versions like UD-Q4_K_XL . We recommend using at least 2-bit dynamic quant UD-Q2_K_XL to balance size and accuracy. If downloads get stuck, see: Hugging Face Hub, XET debugging Copy hf download unsloth/Qwen3.5-35B-A3B-GGUF \ --local-dir unsloth/Qwen3.5-35B-A3B-GGUF \ --include "*MXFP4_MOE*" # Use "*UD-Q2_K_XL*" for Dynamic 2bit Then run the model in conversation mode: Copy ./llama.cpp/llama-cli \ --model unsloth/Qwen3.5-35B-A3B-GGUF/Qwen3.5-35B-A3B-MXFP4_MOE.gguf \ --mmproj unsloth/Qwen3.5-35B-A3B-GGUF/mmproj-F16.gguf \ --seed 3407 \ --temp 1.0 \ --top-p 0.95 \ --min-p 0.01 \ --top-k 40 https://unsloth.ai/docs/models/qwen3.5#qwen3.5-35b-a3b

[Assistant]
The user is showing me the documentation for Qwen3.5-35B-A3B which explains that to disable thinking/reasoning mode, you need to pass `--chat-template-kwargs "{\"enable_thinking\": false}"` to llama.cpp.

So the fix is to add this parameter to the llama-swap configuration for Qwen. Let me find where the model is configured in llama-swap.

Got it. The fix is `--chat-template-kwargs "{\"enable_thinking\": false}"` for llama.cpp.

exec

grep -r "qwen3.5-35b-a3b" /home/crogers2287/llama-swap/ 2>/dev/null | head -20

[Assistant] Let me look at the config to see how Qwen is configured and where I need to add the chat-template-kwargs.

[Assistant]
I can see the Qwen 3.5 35B config. It already has `--reasoning-budget 0 --reasoning-format none` but that's not enough - it needs `--chat-template-kwargs "{\"enable_thinking\": false}"` according to the docs.

Let me add that parameter to the config.

I see it already has `--reasoning-budget 0 --reasoning-format none` but needs the chat-template-kwargs. Let me add that:

edit

    qwen3.5-35b-a3b:
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none

    qwen3.5-35b-a3b:
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --chat-template-kwargs "{\"enable_thinking\": false}" --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none

[Assistant]
Now I need to restart llama-swap to apply the change. Let me check how it's running.

exec

systemctl --user status llama-swap 2>/dev/null || systemctl status llama-swap 2>/dev/null || pgrep -a llama-swap

[Assistant]
I can see llama-swap has `--watch-config` so it should pick up the change automatically. But looking at the running process, it's still using the old command line (without the chat-template-kwargs).

The `--watch-config` watches for config changes, but the model is already running. I need to restart the service to fully apply the new parameters.

sudo systemctl restart llama-swap && sleep 3 && systemctl status llama-swap --no-pager

[Assistant] The restart is running. Let me wait for it.

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2734",
  "sender": "6558388607"
}
```

also, checkout https://github.com/ggml-org/llama.cpp/blob/master/tools/llama-bench/README.md see if we can use this to find the optimum settings for our hardware

[Assistant]
Two things:
1. llama-swap failed to start - need to check the logs
2. User wants me to check the llama-bench README for optimizing settings

Let me check the failure first, then look at the benchmark tool.

journalctl -u llama-swap -n 30 --no-pager

[Assistant]
The YAML config has a syntax error on line 31 - the chat-template-kwargs needs proper quoting in YAML. Let me fix the config file first, then look at the benchmark.

The issue is the JSON string in YAML needs proper escaping. The double quotes inside the JSON need to be escaped or I need to use single quotes around the whole thing.

YAML syntax error - the JSON needs proper quoting. Let me fix it:

[Assistant] The JSON with escaped quotes is fine in the value itself, but YAML is interpreting the `:` inside the JSON as a YAML key-value separator. I need to wrap the entire arg in single quotes.

[Assistant]
Good. Now let me restart llama-swap and verify it works.

sudo systemctl restart llama-swap && sleep 4 && systemctl status llama-swap --no-pager -l

[Assistant]
Still failing. Let me check the specific error.

journalctl -u llama-swap -n 5 --no-pager 2>&1

[Assistant]
Still failing on line 31. Let me look at line 31 specifically to see what's going on.

sed -n '28,35p' /home/crogers2287/llama-swap/config.yaml | cat -A

[Assistant]
The issue is the colon (`:`) inside `'{"enable_thinking": false}'`. Even though it's in single quotes, YAML still sees the colon and gets confused. I need to use a YAML block scalar or wrap the whole cmd value.

Actually, looking at this more carefully, the problem is that YAML is parsing `{"enable_thinking": false}` and seeing the colon as a YAML key-value separator. Single quotes should work but maybe there's an issue with how the command is structured.

Let me try using a YAML literal block or just escaping differently. Actually, looking at the other entries, they don't have this problem because they don't have colons in their values.

One solution: use `--chat-template-kwargs "{\"enable_thinking\":false}"` (no space after colon) but that may still not work.

Better solution: use a literal block scalar with `|` or just quote the entire value.

Let me check what llama.cpp expects for the argument format. Actually, the simplest fix is to just remove the space after the colon: `"enable_thinking":false` instead of `"enable_thinking": false`.

Actually wait - the single quotes should work. Let me try wrapping the whole cmd in double quotes.

Actually no, the issue is that in YAML, even inside a plain scalar value, `:` followed by a space is interpreted as a key-value separator. The single quotes around the JSON should protect it, but it seems they're not being recognized properly.

Let me try escaping with backslashes: `"{\"enable_thinking\":false}"` (no spaces around the colon).

YAML is choking on the colon inside the JSON. Let me try with no space after the colon:

[Assistant]
Hmm, that might not be enough. YAML sees `:` followed by something and gets confused regardless of spacing. Let me try a different approach - use the hex escape or just wrap the entire cmd value.

python3 -c "import yaml; yaml.safe_load(open('/home/crogers2287/llama-swap/config.yaml'))" 2>&1 | head -5

[Assistant]
Python parsed it successfully! So YAML is valid now. Let me restart llama-swap.

Python says it's valid YAML now. Restarting:

sudo systemctl restart llama-swap && sleep 3 && systemctl status llama-swap --no-pager | head -20

[Assistant]
llama-swap is running now. Let me do a quick test to verify Qwen responds properly without the reasoning_content issue, then I'll summarize the llama-bench info for optimizing settings.

llama-swap is back. Let me test Qwen now and verify it outputs to content instead of reasoning_content:

curl -s http://localhost:9069/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.5-35b-a3b","messages":[{"role":"user","content":"Say hello"}],"max_tokens":50}' | jq -r '.choices[0].message.content' 2>/dev/null | head -5

[Assistant]
Hmm, the output is empty or got truncated. The response shows "Thinking Process:" which suggests the model is still outputting reasoning. Let me check the full response.

curl -s http://localhost:9069/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.5-35b-a3b","messages":[{"role":"user","content":"Say hello"}],"max_tokens":100}' 2>/dev/null | jq '.'

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2731",
  "sender": "6558388607"
}
```

To disable thinking / reasoning, use --chat-template-kwargs "{\"enable_thinking\": false}" Qwen3.5-35B-A3B For this guide we will be utilizing Dynamic 4-bit which works great on a 24GB RAM / Mac device for fast inference. Because the model is only around 72GB at full F16 precision, we won't need to worry much about performance. GGUF: Qwen3.5-35B-A3B-GGUF ✨ Run in llama.cpp Obtain the latest llama.cpp on GitHub here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. Copy apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggml-org/llama.cpp cmake llama.cpp -B llama.cpp/build \ -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split cp llama.cpp/build/bin/llama-* llama.cpp If you want to use llama.cpp directly to load models, you can do the below: (:Q3_K_XL) is the quantization type. You can also download via Hugging Face (point 3). This is similar to ollama run . Use export LLAMA_CACHE="folder" to force llama.cpp to save to a specific location. The model has a maximum of 256K context length. Follow one of the specific commands below, according to your use-case: Thinking mode: Precise coding tasks (e.g. WebDev): Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 Non-thinking mode: General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.7 \ --top-p 0.8 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Reasoning tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Download the model via (after installing pip install huggingface_hub hf_transfer ). You can choose UD-Q4_K_XL (dynamic 4bit) or other quantized versions like UD-Q4_K_XL . We recommend using at least 2-bit dynamic quant UD-Q2_K_XL to balance size and accuracy. If downloads get stuck, see: Hugging Face Hub, XET debugging Copy hf download unsloth/Qwen3.5-35B-A3B-GGUF \ --local-dir unsloth/Qwen3.5-35B-A3B-GGUF \ --include "*MXFP4_MOE*" # Use "*UD-Q2_K_XL*" for Dynamic 2bit Then run the model in conversation mode: Copy ./llama.cpp/llama-cli \ --model unsloth/Qwen3.5-35B-A3B-GGUF/Qwen3.5-35B-A3B-MXFP4_MOE.gguf \ --mmproj unsloth/Qwen3.5-35B-A3B-GGUF/mmproj-F16.gguf \ --seed 3407 \ --temp 1.0 \ --top-p 0.95 \ --min-p 0.01 \ --top-k 40 https://unsloth.ai/docs/models/qwen3.5#qwen3.5-35b-a3b

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2731",
  "sender": "6558388607"
}
```

To disable thinking / reasoning, use --chat-template-kwargs "{\"enable_thinking\": false}" Qwen3.5-35B-A3B For this guide we will be utilizing Dynamic 4-bit which works great on a 24GB RAM / Mac device for fast inference. Because the model is only around 72GB at full F16 precision, we won't need to worry much about performance. GGUF: Qwen3.5-35B-A3B-GGUF ✨ Run in llama.cpp Obtain the latest llama.cpp on GitHub here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. Copy apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggml-org/llama.cpp cmake llama.cpp -B llama.cpp/build \ -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split cp llama.cpp/build/bin/llama-* llama.cpp If you want to use llama.cpp directly to load models, you can do the below: (:Q3_K_XL) is the quantization type. You can also download via Hugging Face (point 3). This is similar to ollama run . Use export LLAMA_CACHE="folder" to force llama.cpp to save to a specific location. The model has a maximum of 256K context length. Follow one of the specific commands below, according to your use-case: Thinking mode: Precise coding tasks (e.g. WebDev): Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 Non-thinking mode: General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.7 \ --top-p 0.8 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Reasoning tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Download the model via (after installing pip install huggingface_hub hf_transfer ). You can choose UD-Q4_K_XL (dynamic 4bit) or other quantized versions like UD-Q4_K_XL . We recommend using at least 2-bit dynamic quant UD-Q2_K_XL to balance size and accuracy. If downloads get stuck, see: Hugging Face Hub, XET debugging Copy hf download unsloth/Qwen3.5-35B-A3B-GGUF \ --local-dir unsloth/Qwen3.5-35B-A3B-GGUF \ --include "*MXFP4_MOE*" # Use "*UD-Q2_K_XL*" for Dynamic 2bit Then run the model in conversation mode: Copy ./llama.cpp/llama-cli \ --model unsloth/Qwen3.5-35B-A3B-GGUF/Qwen3.5-35B-A3B-MXFP4_MOE.gguf \ --mmproj unsloth/Qwen3.5-35B-A3B-GGUF/mmproj-F16.gguf \ --seed 3407 \ --temp 1.0 \ --top-p 0.95 \ --min-p 0.01 \ --top-k 40 https://unsloth.ai/docs/models/qwen3.5#qwen3.5-35b-a3b

[User]
System: [2026-02-25 16:14:26 UTC] Exec completed (keen-fjo, code 0) :: <think> Thinking Process: 1. **Analyze the Request:** * Input: "Say hello"

Conversation info (untrusted metadata):
```json
{
  "message_id": "2740",
  "sender": "6558388607"
}
```

its still outputting tts instead of text, we dont need tts in telegram

[Assistant]
The user is saying the local model (Qwen) is still outputting TTS instead of text in Telegram. Looking at the test output I just ran, the model IS outputting to `content` now (not `reasoning_content`), but it's starting with `<think>` tags and thinking process.

The issue isn't about `reasoning_content` vs `content` - the model IS outputting to content. The problem is:
1. The thinking is still happening (it outputs `<think>` tags with the reasoning process)
2. OpenClaw might be interpreting something and triggering TTS

Let me check what's happening. The `--chat-template-kwargs '{"enable_thinking":false}'` should disable the thinking, but it's still showing up. Maybe the issue is:
1. The chat-template-kwargs isn't being passed correctly by llama-swap
2. Or there's something else going on

Let me check the actual command being run and also look at what OpenClaw might be doing with TTS.

Actually, looking more carefully at the output - the content field contains `<think>` tags with the thinking process. The `enable_thinking: false` should suppress this, but it's not working.

Maybe the llama.cpp version doesn't support this flag, or the flag needs to be passed differently through the API.

Let me also check if there's something in OpenClaw's config about TTS auto-triggering.

Two issues:

1. **Qwen still thinking** - the `enable_thinking: false` isn't taking effect (still seeing `<think conteúdo` tags in output)
2. **TTS triggering** - something in OpenClaw is calling TTS instead of just returning text

Let me check the llama.cpp version and OpenClaw's TTS behavior:

/mnt/storage/llama.cpp-nemotron/build/bin/llama-server --version 2>&1 | head -3

grep -r "tts\|TTS" ~/.openclaw/config.* 2>/dev/null | head -10

[Assistant]
The llama-server doesn't show version info easily. Let me check if it's a recent build and supports the enable_thinking flag. The issue is the model is still outputting thinking content.

Let me try a different approach - maybe I need to use `--jinja` flag and/or a custom template like we did with nanbeige.

Also, the TTS issue - let me check if there's something in the agent config that might be triggering TTS.

ls -la /mnt/storage/llama.cpp-nemotron/build/bin/llama-server && stat -c "%y" /mnt/storage/llama.cpp-nemotron/build/bin/llama-server

cat ~/.openclaw/agents/clinker/config.yaml 2>/dev/null | head -50

[Assistant]
The llama-server was built on Feb 25 2026 - that's very recent, so it should support the enable_thinking flag.

Let me check if the flag is actually being passed to the server when it starts up. The issue might be that llama-swap isn't passing the chat-template-kwargs correctly.

Let me also check what's happening with TTS - maybe there's an issue with how the model response is being processed by OpenClaw.

ps aux | grep "qwen3.5-35b" | grep -v grep

cat ~/.openclaw/config.yaml 2>/dev/null | grep -A5 -B5 -i tts

[Assistant]
No qwen3.5-35b running right now (llama-swap only loads on demand). Let me check the gateway config for TTS settings.

cat ~/.openclaw/gateway.yaml 2>/dev/null | grep -A10 -i "tts\|voice\|speech" || echo "No TTS config in gateway.yaml"

openclaw config get 2>/dev/null | grep -i tts || echo "No tts in openclaw config"

[Assistant]
No TTS config found. The TTS might be being called by the model itself or by some agent logic. Let me check if the agent has TTS enabled or if there's something in SOUL.md or other config.

Actually, looking at the tools available, there's a `tts` tool. Maybe the model is calling the TTS tool instead of just responding with text.

Let me look at the agent's behavior and see if there's a default TTS behavior.

Actually wait - the user said "local model keeps responding with only tts". This could mean:
1. The model is calling the TTS tool
2. Or the model output is somehow being converted to TTS

Let me check if the agent has some TTS auto-behavior configured.

grep -r "tts\|TTS" ~/.openclaw/agents/clinker/ 2>/dev/null

cat ~/.openclaw/agents/clinker/SOUL.md | grep -i tts || echo "No TTS in SOUL.md"

[Assistant]
Okay so looking at the grep results, I don't see any direct TTS configuration in SOUL.md. The issue is that the model is outputting to `content` (good) but the content still starts with `cts` and thinking process text.

Let me re-read the user's message more carefully. They said "its still outputting tts instead of text, we dont need tts in telegram"

I think the user is saying that the model output (which contains thinking tags like `öh`) is being interpreted somehow as TTS-related.

Looking at the test output I ran:
```json
"content": "öh\nThinking Process:\n\n1.  **Analyze the Request:** The user is saying \"Say hello\". This is a simple, direct greeting.\n..."
```

The model IS outputting to `content`, but the content has:
1. `öh` tags
2. A "Thinking Process" section

The `enable_thinking: false` should have suppressed this but it didn't work.

Let me check:
1. If the chat-template-kwargs is being passed correctly
2. Try a different approach - maybe use a custom jinja template like we did for nanbeige

Also, for the llama-bench README the user shared, let me read that to help with performance tuning.

I see the issue now. The model IS outputting to `content`, but it's still doing the thinking process inside `öh` tags. The `enable_thinking: false` flag isn't working with the current llama.cpp version.

Let me try a custom jinja template approach like we did for nanbeige:

cat > /home/crogers2287/llama-swap/qwen35-nothink.jinja << 'EOF'
{%- if tools %}
    {{- '<|im_start|>system\n' }}
    {%- if messages[0]['role'] == 'system' %}
        {{- messages[0]['content'] + '\n\n' }}
    {%- endif %}
    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\nYou are provided with function descriptions in XML format:\n<tools>\n" }}
    {%- for tool in tools %}
        {{- tool | tojson(indent=4) + '\n\n' }}
    {%- endfor %}
    {{- "</tools>\n\nFor each function call, output the function name and arguments in XML format:\n<function=\"name\">\n<parameter=\"name\">value</parameter>\n</function>" }}
    {{- '<|im_end|>\n' }}
{%- elif messages[0]['role'] == 'system' %}
    {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- endif %}
{%- for message in messages %}
    {%- if message['role'] == 'user' %}
        {{- '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }}
    {%- elif message['role'] == 'assistant' %}
        {%- if message.get('content') %}
            {{- '<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}
        {%- endif %}
    {%- elif message['role'] == 'tool' %}
        {{- '<|im_start|>tool\n<tool_response>\n' + message['content'] + '\n</tool_response>\n<|im_end|>\n' }}
    {%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
    {{- '<|im_start|>assistant\n' }}
{%- endif %}
EOF
echo "Created qwen35-nothink.jinja"

[Assistant]
Now let me update the config to use this custom template that doesn't include thinking tags:

edit

cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template-file /home/crogers2287/llama-swap/qwen35-nothink.jinja --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none

cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --chat-template-kwargs '{"enable_thinking":false}' --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none

[Assistant]
Now let me restart llama-swap and test again. But first, let me also check the llama-bench README to help with performance tuning.

exec

sudo systemctl restart llama-swap && sleep 3 && systemctl status llama-swap --no-pager | head -15

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2743",
  "sender": "6558388607"
}
```

still tts

[User]
System: [2026-02-25 16:38:26 UTC] Model switched to minimax/MiniMax-M2.5-highspeed.

Conversation info (untrusted metadata):
```json
{
  "message_id": "2757",
  "sender": "6558388607"
}
```

ok, so the local qwen model keeps outputting tts

[User]
System: [2026-02-25 16:40:39 UTC] Model switched to google/gemini-3-flash-preview.

Conversation info (untrusted metadata):
```json
{
  "message_id": "2762",
  "sender": "6558388607"
}
```

still tts issues

[Assistant]
**Troubleshooting Qwen Output**

I'm still grappling with the Qwen model's persistent TTS output in Telegram. My attempts to modify the chat template kwargs haven't yielded the desired results. I'm going to look at the other model configurations to see if the issue is there.

**Investigating TTS Trigger**

I've been trying to solve the Qwen model's TTS behavior. While a previous Jinja template was implemented to remove thinking tags, the user is still reporting "tts". Looking deeper, I noticed the `llama-swap` logs didn't contain an error. Now, I'm focusing on why the model might be *calling* a `tts` tool based on its system prompt instructions.

journalctl -u llama-swap -n 50 --no-pager

[Assistant]
**Analyzing Streaming Errors**

I've been digging into those logs, and it looks like we're still having issues with streaming. The client disconnection during streaming, paired with the "no valid JSON data" error, points to two major areas: model outputting malformed data or some hiccup in the stream itself. I suspect either the model's output isn't consistently valid JSON, or there are interruptions in the data transmission.

cat /home/crogers2287/.openclaw/agents/clinker/config.yaml 2>/dev/null || echo "No agent config.yaml"

[Assistant]
**Investigating Config Options**

I've examined the configuration and haven't found any global Text-to-Speech setting overriding local model behavior. Now, I'm focusing on the model list, specifically the provided example, to see if there are any hints related to Text-to-Speech in that section. It's a key spot to probe.

**Analyzing Template Impact**

I've discovered the `nanbeige` alias is associated with a specific custom template, `qwen35-nothink.jinja`, in the `llama-swap` config. I'm now investigating whether this template is correctly stripping thinking tags, as this could be the root cause of the persistent Text-to-Speech output. My next step involves inspecting the server response to check for the presence of these tags and the implications for OpenClaw's interpretation.

[Assistant] ✅ New session started · model: local/qwen3.5-35b-a3b

[User]
System: [2026-02-25 15:50:20 UTC] ⚠ Post-Compaction Audit: The following required startup files were not read after context reset:
  - WORKFLOW_AUTO.md
  - memory\/\d{4}-\d{2}-\d{2}\.md

Please read them now using the Read tool before continuing. This ensures your operating protocols are restored after memory compaction.

A new session was started via /new or /reset. Greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning.

[Assistant] Question: 2026-02-25

[User]
System: [2026-02-25 15:57:55 UTC] Model switched to GLM (zai/glm-5).

Conversation info (untrusted metadata):
```json
{
  "message_id": "2729",
  "sender": "6558388607"
}
```

why does the local model keep responding with only tts?

[Assistant]
The user is asking why the local model keeps responding with only TTS (text-to-speech). This is a technical question about model behavior. Let me check memory for any relevant context about this issue.

Question: local model TTS responding text to speech nanbeige qwen

[Assistant]
The memory search didn't directly reveal the TTS issue. Let me search more specifically for this problem.

Question: TTS only response empty content tool call NO_REPLY

[Assistant]
Still not finding the specific TTS issue. Let me check MEMORY.md for more context on the model behavior issue.

Question: reasoning_content output content assistant generation template

[Assistant] I found a reference in MEMORY.md about Nanbeige outputting to `reasoning_content` instead of `content`. Let me look at that section more closely since it might be related.

[Assistant]
Let me search for the Nanbeige fix section more specifically.

Question: nanbeige fix reasoning content template jinja nothink

[Assistant]
Okay, I found the relevant info. The Nanbeige fix is about `reasoning_content` vs `content`, not TTS. But the user is asking specifically about the local model responding with only TTS.

Let me check the agent config or look at recent issues. The user says the local model keeps responding with "only tts" - this could mean:
1. The model is calling the TTS tool instead of responding with text
2. The model outputs empty content but triggers TTS somehow

Let me check if there's something in the config or recent sessions about this.

Found it. The issue is **Qwen 3.5 35B** — it's a reasoning model that outputs to `reasoning_content` instead of `content`, so OpenClaw sees an empty response and may be defaulting to TTS or some other fallback behavior.

This is the same class of bug we fixed with Nanbeige (see MEMORY.md lines 198-210).

**Quick fix options:**

1. **Apply a similar chat template** for Qwen 3.5 to force it to output to `content` instead of `reasoning_content`

2. **Switch default model** to something that doesn't have this reasoning output split (GLM-5, Nemotron, or a non-reasoning variant)

Which would you prefer? If you want to keep using Qwen locally, I'll need to create a jinja template like `/home/crogers2287/llama-swap/qwen-nothink.jinja` to fix the output routing.

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2731",
  "sender": "6558388607"
}
```

To disable thinking / reasoning, use --chat-template-kwargs "{\"enable_thinking\": false}" Qwen3.5-35B-A3B For this guide we will be utilizing Dynamic 4-bit which works great on a 24GB RAM / Mac device for fast inference. Because the model is only around 72GB at full F16 precision, we won't need to worry much about performance. GGUF: Qwen3.5-35B-A3B-GGUF ✨ Run in llama.cpp Obtain the latest llama.cpp on GitHub here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. Copy apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggml-org/llama.cpp cmake llama.cpp -B llama.cpp/build \ -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split cp llama.cpp/build/bin/llama-* llama.cpp If you want to use llama.cpp directly to load models, you can do the below: (:Q3_K_XL) is the quantization type. You can also download via Hugging Face (point 3). This is similar to ollama run . Use export LLAMA_CACHE="folder" to force llama.cpp to save to a specific location. The model has a maximum of 256K context length. Follow one of the specific commands below, according to your use-case: Thinking mode: Precise coding tasks (e.g. WebDev): Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 Non-thinking mode: General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.7 \ --top-p 0.8 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Reasoning tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Download the model via (after installing pip install huggingface_hub hf_transfer ). You can choose UD-Q4_K_XL (dynamic 4bit) or other quantized versions like UD-Q4_K_XL . We recommend using at least 2-bit dynamic quant UD-Q2_K_XL to balance size and accuracy. If downloads get stuck, see: Hugging Face Hub, XET debugging Copy hf download unsloth/Qwen3.5-35B-A3B-GGUF \ --local-dir unsloth/Qwen3.5-35B-A3B-GGUF \ --include "*MXFP4_MOE*" # Use "*UD-Q2_K_XL*" for Dynamic 2bit Then run the model in conversation mode: Copy ./llama.cpp/llama-cli \ --model unsloth/Qwen3.5-35B-A3B-GGUF/Qwen3.5-35B-A3B-MXFP4_MOE.gguf \ --mmproj unsloth/Qwen3.5-35B-A3B-GGUF/mmproj-F16.gguf \ --seed 3407 \ --temp 1.0 \ --top-p 0.95 \ --min-p 0.01 \ --top-k 40 https://unsloth.ai/docs/models/qwen3.5#qwen3.5-35b-a3b

[Assistant]
The user is showing me the documentation for Qwen3.5-35B-A3B which explains that to disable thinking/reasoning mode, you need to pass `--chat-template-kwargs "{\"enable_thinking\": false}"` to llama.cpp.

So the fix is to add this parameter to the llama-swap configuration for Qwen. Let me find where the model is configured in llama-swap.

Got it. The fix is `--chat-template-kwargs "{\"enable_thinking\": false}"` for llama.cpp.

grep -r "qwen3.5-35b-a3b" /home/crogers2287/llama-swap/ 2>/dev/null | head -20

[Assistant] Let me look at the config to see how Qwen is configured and where I need to add the chat-template-kwargs.

[Assistant]
I can see the Qwen 3.5 35B config. It already has `--reasoning-budget 0 --reasoning-format none` but that's not enough - it needs `--chat-template-kwargs "{\"enable_thinking\": false}"` according to the docs.

Let me add that parameter to the config.

I see it already has `--reasoning-budget 0 --reasoning-format none` but needs the chat-template-kwargs. Let me add that:

edit

    qwen3.5-35b-a3b:
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none

    qwen3.5-35b-a3b:
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --chat-template-kwargs "{\"enable_thinking\": false}" --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none

[Assistant]
Now I need to restart llama-swap to apply the change. Let me check how it's running.

exec

systemctl --user status llama-swap 2>/dev/null || systemctl status llama-swap 2>/dev/null || pgrep -a llama-swap

[Assistant]
I can see llama-swap has `--watch-config` so it should pick up the change automatically. But looking at the running process, it's still using the old command line (without the chat-template-kwargs).

The `--watch-config` watches for config changes, but the model is already running. I need to restart the service to fully apply the new parameters.

sudo systemctl restart llama-swap && sleep 3 && systemctl status llama-swap --no-pager

[Assistant] The restart is running. Let me wait for it.

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2734",
  "sender": "6558388607"
}
```

also, checkout https://github.com/ggml-org/llama.cpp/blob/master/tools/llama-bench/README.md see if we can use this to find the optimum settings for our hardware

[Assistant]
Two things:
1. llama-swap failed to start - need to check the logs
2. User wants me to check the llama-bench README for optimizing settings

Let me check the failure first, then look at the benchmark tool.

journalctl -u llama-swap -n 30 --no-pager

[Assistant]
The YAML config has a syntax error on line 31 - the chat-template-kwargs needs proper quoting in YAML. Let me fix the config file first, then look at the benchmark.

The issue is the JSON string in YAML needs proper escaping. The double quotes inside the JSON need to be escaped or I need to use single quotes around the whole thing.

YAML syntax error - the JSON needs proper quoting. Let me fix it:

[Assistant] The JSON with escaped quotes is fine in the value itself, but YAML is interpreting the `:` inside the JSON as a YAML key-value separator. I need to wrap the entire arg in single quotes.

[Assistant]
Good. Now let me restart llama-swap and verify it works.

sudo systemctl restart llama-swap && sleep 4 && systemctl status llama-swap --no-pager -l

[Assistant]
Still failing. Let me check the specific error.

journalctl -u llama-swap -n 5 --no-pager 2>&1

[Assistant]
Still failing on line 31. Let me look at line 31 specifically to see what's going on.

sed -n '28,35p' /home/crogers2287/llama-swap/config.yaml | cat -A

[Assistant]
The issue is the colon (`:`) inside `'{"enable_thinking": false}'`. Even though it's in single quotes, YAML still sees the colon and gets confused. I need to use a YAML block scalar or wrap the whole cmd value.

Actually, looking at this more carefully, the problem is that YAML is parsing `{"enable_thinking": false}` and seeing the colon as a YAML key-value separator. Single quotes should work but maybe there's an issue with how the command is structured.

Let me try using a YAML literal block or just escaping differently. Actually, looking at the other entries, they don't have this problem because they don't have colons in their values.

One solution: use `--chat-template-kwargs "{\"enable_thinking\":false}"` (no space after colon) but that may still not work.

Better solution: use a literal block scalar with `|` or just quote the entire value.

Let me check what llama.cpp expects for the argument format. Actually, the simplest fix is to just remove the space after the colon: `"enable_thinking":false` instead of `"enable_thinking": false`.

Actually wait - the single quotes should work. Let me try wrapping the whole cmd in double quotes.

Actually no, the issue is that in YAML, even inside a plain scalar value, `:` followed by a space is interpreted as a key-value separator. The single quotes around the JSON should protect it, but it seems they're not being recognized properly.

Let me try escaping with backslashes: `"{\"enable_thinking\":false}"` (no spaces around the colon).

YAML is choking on the colon inside the JSON. Let me try with no space after the colon:

[Assistant]
Hmm, that might not be enough. YAML sees `:` followed by something and gets confused regardless of spacing. Let me try a different approach - use the hex escape or just wrap the entire cmd value.

python3 -c "import yaml; yaml.safe_load(open('/home/crogers2287/llama-swap/config.yaml'))" 2>&1 | head -5

[Assistant]
Python parsed it successfully! So YAML is valid now. Let me restart llama-swap.

Python says it's valid YAML now. Restarting:

sudo systemctl restart llama-swap && sleep 3 && systemctl status llama-swap --no-pager | head -20

[Assistant]
llama-swap is running now. Let me do a quick test to verify Qwen responds properly without the reasoning_content issue, then I'll summarize the llama-bench info for optimizing settings.

llama-swap is back. Let me test Qwen now and verify it outputs to content instead of reasoning_content:

curl -s http://localhost:9069/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.5-35b-a3b","messages":[{"role":"user","content":"Say hello"}],"max_tokens":50}' | jq -r '.choices[0].message.content' 2>/dev/null | head -5

[Assistant]
Hmm, the output is empty or got truncated. The response shows "Thinking Process:" which suggests the model is still outputting reasoning. Let me check the full response.

curl -s http://localhost:9069/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.5-35b-a3b","messages":[{"role":"user","content":"Say hello"}],"max_tokens":100}' 2>/dev/null | jq '.'

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2731",
  "sender": "6558388607"
}
```

To disable thinking / reasoning, use --chat-template-kwargs "{\"enable_thinking\": false}" Qwen3.5-35B-A3B For this guide we will be utilizing Dynamic 4-bit which works great on a 24GB RAM / Mac device for fast inference. Because the model is only around 72GB at full F16 precision, we won't need to worry much about performance. GGUF: Qwen3.5-35B-A3B-GGUF ✨ Run in llama.cpp Obtain the latest llama.cpp on GitHub here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. Copy apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggml-org/llama.cpp cmake llama.cpp -B llama.cpp/build \ -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split cp llama.cpp/build/bin/llama-* llama.cpp If you want to use llama.cpp directly to load models, you can do the below: (:Q3_K_XL) is the quantization type. You can also download via Hugging Face (point 3). This is similar to ollama run . Use export LLAMA_CACHE="folder" to force llama.cpp to save to a specific location. The model has a maximum of 256K context length. Follow one of the specific commands below, according to your use-case: Thinking mode: Precise coding tasks (e.g. WebDev): Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 Non-thinking mode: General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.7 \ --top-p 0.8 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Reasoning tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Download the model via (after installing pip install huggingface_hub hf_transfer ). You can choose UD-Q4_K_XL (dynamic 4bit) or other quantized versions like UD-Q4_K_XL . We recommend using at least 2-bit dynamic quant UD-Q2_K_XL to balance size and accuracy. If downloads get stuck, see: Hugging Face Hub, XET debugging Copy hf download unsloth/Qwen3.5-35B-A3B-GGUF \ --local-dir unsloth/Qwen3.5-35B-A3B-GGUF \ --include "*MXFP4_MOE*" # Use "*UD-Q2_K_XL*" for Dynamic 2bit Then run the model in conversation mode: Copy ./llama.cpp/llama-cli \ --model unsloth/Qwen3.5-35B-A3B-GGUF/Qwen3.5-35B-A3B-MXFP4_MOE.gguf \ --mmproj unsloth/Qwen3.5-35B-A3B-GGUF/mmproj-F16.gguf \ --seed 3407 \ --temp 1.0 \ --top-p 0.95 \ --min-p 0.01 \ --top-k 40 https://unsloth.ai/docs/models/qwen3.5#qwen3.5-35b-a3b

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2731",
  "sender": "6558388607"
}
```

To disable thinking / reasoning, use --chat-template-kwargs "{\"enable_thinking\": false}" Qwen3.5-35B-A3B For this guide we will be utilizing Dynamic 4-bit which works great on a 24GB RAM / Mac device for fast inference. Because the model is only around 72GB at full F16 precision, we won't need to worry much about performance. GGUF: Qwen3.5-35B-A3B-GGUF ✨ Run in llama.cpp Obtain the latest llama.cpp on GitHub here. You can follow the build instructions below as well. Change -DGGML_CUDA=ON to -DGGML_CUDA=OFF if you don't have a GPU or just want CPU inference. Copy apt-get update apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y git clone https://github.com/ggml-org/llama.cpp cmake llama.cpp -B llama.cpp/build \ -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split cp llama.cpp/build/bin/llama-* llama.cpp If you want to use llama.cpp directly to load models, you can do the below: (:Q3_K_XL) is the quantization type. You can also download via Hugging Face (point 3). This is similar to ollama run . Use export LLAMA_CACHE="folder" to force llama.cpp to save to a specific location. The model has a maximum of 256K context length. Follow one of the specific commands below, according to your use-case: Thinking mode: Precise coding tasks (e.g. WebDev): Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.6 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 Non-thinking mode: General tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 0.7 \ --top-p 0.8 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Reasoning tasks: Copy export LLAMA_CACHE="unsloth/Qwen3.5-35B-A3B-GGUF" ./llama.cpp/llama-cli \ -hf unsloth/Qwen3.5-35B-A3B-GGUF:MXFP4_MOE \ --ctx-size 16384 \ --temp 1.0 \ --top-p 0.95 \ --top-k 20 \ --min-p 0.00 \ --chat-template-kwargs "{\"enable_thinking\": false}" Download the model via (after installing pip install huggingface_hub hf_transfer ). You can choose UD-Q4_K_XL (dynamic 4bit) or other quantized versions like UD-Q4_K_XL . We recommend using at least 2-bit dynamic quant UD-Q2_K_XL to balance size and accuracy. If downloads get stuck, see: Hugging Face Hub, XET debugging Copy hf download unsloth/Qwen3.5-35B-A3B-GGUF \ --local-dir unsloth/Qwen3.5-35B-A3B-GGUF \ --include "*MXFP4_MOE*" # Use "*UD-Q2_K_XL*" for Dynamic 2bit Then run the model in conversation mode: Copy ./llama.cpp/llama-cli \ --model unsloth/Qwen3.5-35B-A3B-GGUF/Qwen3.5-35B-A3B-MXFP4_MOE.gguf \ --mmproj unsloth/Qwen3.5-35B-A3B-GGUF/mmproj-F16.gguf \ --seed 3407 \ --temp 1.0 \ --top-p 0.95 \ --min-p 0.01 \ --top-k 40 https://unsloth.ai/docs/models/qwen3.5#qwen3.5-35b-a3b

[User]
System: [2026-02-25 16:14:26 UTC] Exec completed (keen-fjo, code 0) :: <think> Thinking Process: 1. **Analyze the Request:** * Input: "Say hello"

Conversation info (untrusted metadata):
```json
{
  "message_id": "2740",
  "sender": "6558388607"
}
```

its still outputting tts instead of text, we dont need tts in telegram

[Assistant]
The user is saying the local model (Qwen) is still outputting TTS instead of text in Telegram. Looking at the test output I just ran, the model IS outputting to `content` now (not `reasoning_content`), but it's starting with `<think>` tags and thinking process.

The issue isn't about `reasoning_content` vs `content` - the model IS outputting to content. The problem is:
1. The thinking is still happening (it outputs `<think>` tags with the reasoning process)
2. OpenClaw might be interpreting something and triggering TTS

Let me check what's happening. The `--chat-template-kwargs '{"enable_thinking":false}'` should disable the thinking, but it's still showing up. Maybe the issue is:
1. The chat-template-kwargs isn't being passed correctly by llama-swap
2. Or there's something else going on

Let me check the actual command being run and also look at what OpenClaw might be doing with TTS.

Actually, looking more carefully at the output - the content field contains `<think>` tags with the thinking process. The `enable_thinking: false` should suppress this, but it's not working.

Maybe the llama.cpp version doesn't support this flag, or the flag needs to be passed differently through the API.

Let me also check if there's something in OpenClaw's config about TTS auto-triggering.

Two issues:

1. **Qwen still thinking** - the `enable_thinking: false` isn't taking effect (still seeing `<think conteúdo` tags in output)
2. **TTS triggering** - something in OpenClaw is calling TTS instead of just returning text

Let me check the llama.cpp version and OpenClaw's TTS behavior:

/mnt/storage/llama.cpp-nemotron/build/bin/llama-server --version 2>&1 | head -3

grep -r "tts\|TTS" ~/.openclaw/config.* 2>/dev/null | head -10

[Assistant]
The llama-server doesn't show version info easily. Let me check if it's a recent build and supports the enable_thinking flag. The issue is the model is still outputting thinking content.

Let me try a different approach - maybe I need to use `--jinja` flag and/or a custom template like we did with nanbeige.

Also, the TTS issue - let me check if there's something in the agent config that might be triggering TTS.

ls -la /mnt/storage/llama.cpp-nemotron/build/bin/llama-server && stat -c "%y" /mnt/storage/llama.cpp-nemotron/build/bin/llama-server

cat ~/.openclaw/agents/clinker/config.yaml 2>/dev/null | head -50

[Assistant]
The llama-server was built on Feb 25 2026 - that's very recent, so it should support the enable_thinking flag.

Let me check if the flag is actually being passed to the server when it starts up. The issue might be that llama-swap isn't passing the chat-template-kwargs correctly.

Let me also check what's happening with TTS - maybe there's an issue with how the model response is being processed by OpenClaw.

ps aux | grep "qwen3.5-35b" | grep -v grep

cat ~/.openclaw/config.yaml 2>/dev/null | grep -A5 -B5 -i tts

[Assistant]
No qwen3.5-35b running right now (llama-swap only loads on demand). Let me check the gateway config for TTS settings.

cat ~/.openclaw/gateway.yaml 2>/dev/null | grep -A10 -i "tts\|voice\|speech" || echo "No TTS config in gateway.yaml"

openclaw config get 2>/dev/null | grep -i tts || echo "No tts in openclaw config"

[Assistant]
No TTS config found. The TTS might be being called by the model itself or by some agent logic. Let me check if the agent has TTS enabled or if there's something in SOUL.md or other config.

Actually, looking at the tools available, there's a `tts` tool. Maybe the model is calling the TTS tool instead of just responding with text.

Let me look at the agent's behavior and see if there's a default TTS behavior.

Actually wait - the user said "local model keeps responding with only tts". This could mean:
1. The model is calling the TTS tool
2. Or the model output is somehow being converted to TTS

Let me check if the agent has some TTS auto-behavior configured.

grep -r "tts\|TTS" ~/.openclaw/agents/clinker/ 2>/dev/null

cat ~/.openclaw/agents/clinker/SOUL.md | grep -i tts || echo "No TTS in SOUL.md"

[Assistant]
Okay so looking at the grep results, I don't see any direct TTS configuration in SOUL.md. The issue is that the model is outputting to `content` (good) but the content still starts with `cts` and thinking process text.

Let me re-read the user's message more carefully. They said "its still outputting tts instead of text, we dont need tts in telegram"

I think the user is saying that the model output (which contains thinking tags like `öh`) is being interpreted somehow as TTS-related.

Looking at the test output I ran:
```json
"content": "öh\nThinking Process:\n\n1.  **Analyze the Request:** The user is saying \"Say hello\". This is a simple, direct greeting.\n..."
```

The model IS outputting to `content`, but the content has:
1. `öh` tags
2. A "Thinking Process" section

The `enable_thinking: false` should have suppressed this but it didn't work.

Let me check:
1. If the chat-template-kwargs is being passed correctly
2. Try a different approach - maybe use a custom jinja template like we did for nanbeige

Also, for the llama-bench README the user shared, let me read that to help with performance tuning.

I see the issue now. The model IS outputting to `content`, but it's still doing the thinking process inside `öh` tags. The `enable_thinking: false` flag isn't working with the current llama.cpp version.

Let me try a custom jinja template approach like we did for nanbeige:

cat > /home/crogers2287/llama-swap/qwen35-nothink.jinja << 'EOF'
{%- if tools %}
    {{- '<|im_start|>system\n' }}
    {%- if messages[0]['role'] == 'system' %}
        {{- messages[0]['content'] + '\n\n' }}
    {%- endif %}
    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\nYou are provided with function descriptions in XML format:\n<tools>\n" }}
    {%- for tool in tools %}
        {{- tool | tojson(indent=4) + '\n\n' }}
    {%- endfor %}
    {{- "</tools>\n\nFor each function call, output the function name and arguments in XML format:\n<function=\"name\">\n<parameter=\"name\">value</parameter>\n</function>" }}
    {{- '<|im_end|>\n' }}
{%- elif messages[0]['role'] == 'system' %}
    {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- endif %}
{%- for message in messages %}
    {%- if message['role'] == 'user' %}
        {{- '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }}
    {%- elif message['role'] == 'assistant' %}
        {%- if message.get('content') %}
            {{- '<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}
        {%- endif %}
    {%- elif message['role'] == 'tool' %}
        {{- '<|im_start|>tool\n<tool_response>\n' + message['content'] + '\n</tool_response>\n<|im_end|>\n' }}
    {%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
    {{- '<|im_start|>assistant\n' }}
{%- endif %}
EOF
echo "Created qwen35-nothink.jinja"

[Assistant]
Now let me update the config to use this custom template that doesn't include thinking tags:

edit

cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template-file /home/crogers2287/llama-swap/qwen35-nothink.jinja --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none

cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --chat-template-kwargs '{"enable_thinking":false}' --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none

[Assistant]
Now let me restart llama-swap and test again. But first, let me also check the llama-bench README to help with performance tuning.

exec

sudo systemctl restart llama-swap && sleep 3 && systemctl status llama-swap --no-pager | head -15

[User]
Conversation info (untrusted metadata):
```json
{
  "message_id": "2743",
  "sender": "6558388607"
}
```

still tts

[User]
System: [2026-02-25 16:38:26 UTC] Model switched to minimax/MiniMax-M2.5-highspeed.

Conversation info (untrusted metadata):
```json
{
  "message_id": "2757",
  "sender": "6558388607"
}
```

ok, so the local qwen model keeps outputting tts

[User]
System: [2026-02-25 16:40:39 UTC] Model switched to google/gemini-3-flash-preview.

Conversation info (untrusted metadata):
```json
{
  "message_id": "2762",
  "sender": "6558388607"
}
```

still tts issues

[Assistant]
**Troubleshooting Qwen Output**

I'm still grappling with the Qwen model's persistent TTS output in Telegram. My attempts to modify the chat template kwargs haven't yielded the desired results. I'm going to look at the other model configurations to see if the issue is there.

**Investigating TTS Trigger**

I've been trying to solve the Qwen model's TTS behavior. While a previous Jinja template was implemented to remove thinking tags, the user is still reporting "tts". Looking deeper, I noticed the `llama-swap` logs didn't contain an error. Now, I'm focusing on why the model might be *calling* a `tts` tool based on its system prompt instructions.

journalctl -u llama-swap -n 50 --no-pager

[Assistant]
**Analyzing Streaming Errors**

I've been digging into those logs, and it looks like we're still having issues with streaming. The client disconnection during streaming, paired with the "no valid JSON data" error, points to two major areas: model outputting malformed data or some hiccup in the stream itself. I suspect either the model's output isn't consistently valid JSON, or there are interruptions in the data transmission.

cat /home/crogers2287/.openclaw/agents/clinker/config.yaml 2>/dev/null || echo "No agent config.yaml"

[Assistant]
**Investigating Config Options**

I've examined the configuration and haven't found any global Text-to-Speech setting overriding local model behavior. Now, I'm focusing on the model list, specifically the provided example, to see if there are any hints related to Text-to-Speech in that section. It's a key spot to probe.

**Analyzing Template Impact**

I've discovered the `nanbeige` alias is associated with a specific custom template, `qwen35-nothink.jinja`, in the `llama-swap` config. I'm now investigating whether this template is correctly stripping thinking tags, as this could be the root cause of the persistent Text-to-Speech output. My next step involves inspecting the server response to check for the presence of these tags and the implications for OpenClaw's interpretation.


**Analyzing Model Behavior**

I've been examining the session history and observed that `qwen3.5-35b-a3b` initially made tool calls for `read` and `memory_search`. Then, after a specific user message, the user switched away from the local model. I'm trying to see if I can isolate the causal factor.

ls /usr/lib/node_modules/opengoat/dist/openclaw-plugin/ 2>/dev/null || ls /usr/lib/node_modules/openclaw/dist/plugins/ 2>/dev/null


index.ts
openclaw.plugin.json
package.json
src


OpenGoat

Expose OpenGoat CLI through OpenClaw as an alternative execution path.

Executable path or command name used to launch OpenGoat.

Arguments prepended to every forwarded OpenGoat invocation.

Optional working directory for OpenGoat command execution.

String map merged into process environment for OpenGoat runs.


**Investigating TTS Setup**

I'm currently focused on determining why a potential TTS plugin isn't behaving as expected. I'm now examining the `telegram` channel configuration within the gateway settings to see if an auto-TTS setting is enabled. It seems the plugin may not be configured, or may not exist at all. I will continue to review settings.


{%- if tools %}
    {{- '<|im_start|>system\n' }}
    {%- if messages[0]['role'] == 'system' %}
        {{- messages[0]['content'] + '\n\n' }}
    {%- endif %}
    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\nYou are provided with function descriptions in XML format:\n<tools>\n" }}
    {%- for tool in tools %}
        {{- tool | tojson(indent=4) + '\n\n' }}
    {%- endfor %}
    {{- "</tools>\n\nFor each function call, output the function name and arguments in XML format:\n<function=\"name\">\n<parameter=\"name\">value</parameter>\n</function>" }}
    {{- '<|im_end|>\n' }}
{%- elif messages[0]['role'] == 'system' %}
    {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- endif %}
{%- for message in messages %}
    {%- if message['role'] == 'user' %}
        {{- '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }}
    {%- elif message['role'] == 'assistant' %}
        {%- if message.get('content') %}
            {{- '<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}
        {%- endif %}
    {%- elif message['role'] == 'tool' %}
        {{- '<|im_start|>tool\n<tool_response>\n' + message['content'] + '\n</tool_response>\n<|im_end|>\n' }}
    {%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
    {{- '<|im_start|>assistant\n' }}
{%- endif %}


**Investigating Delivery Method**

I think I've figured out why the model seems to favor text-to-speech outputs. It seems plausible that if the model believes it's addressing a voice query, it automatically defaults to calling the `tts` tool. Despite this potential connection, no evidence of tool calls appears in the historical data. The delivery mechanism is next on the list.


Telegram: configured
- canna (token:config)
- carl (token:config)
- clinker (token:config)
- grant (token:config)
- magoo (token:config)
- maya (token:config)
- selena (token:config)
- twain (token:config)
Email: configured
- default

Attack surface summary

groups: open=1, allowlist=0
tools.elevated: enabled
hooks.webhooks: disabled
hooks.internal: enabled
browser control: enabled

No auth rate limiting configured

gateway.bind is not loopback but no gateway.auth.rateLimit is configured. Without rate limiting, brute-force auth attacks are not mitigated.

Set gateway.auth.rateLimit (e.g. { maxAttempts: 10, windowMs: 60000, lockoutMs: 300000 }).

HTTP API session-key override is enabled

/v1/chat/completions accept x-openclaw-session-key for per-request session routing. Treat API credential holders as trusted principals.

Gateway password is stored in config

gateway.auth.password is set in the config file; prefer environment variables for secrets when possible.

Prefer OPENCLAW_GATEWAY_PASSWORD (env) and remove gateway.auth.password from disk.

Small models require sandboxing and web tools disabled

Small models (<=300B params) detected:
- local/qwen3.5-35b-a3b (35B) @ agents.defaults.model.fallbacks (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL (30B) @ agents.defaults.model.fallbacks (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.grant.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.maya.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.magoo.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.ghc-marketing.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.cannadeals-marketing.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.cardone.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.clinker.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.mark-twain.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.carl.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.selena.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
- local/qwen3.5-35b-a3b (35B) @ agents.list.ceo.model.primary (unsafe; sandbox=off; web=[web_search, web_fetch, browser])
Uncontrolled input tools allowed: web_search, web_fetch, browser.
Small models are not recommended for untrusted inputs.

If you must use small models, enable sandboxing for all sessions (agents.defaults.sandbox.mode="all") and disable web_search/web_fetch/browser (tools.deny=["group:web","browser"]).

Open groupPolicy with elevated tools enabled

Found groupPolicy="open" at:
- channels.telegram.groupPolicy
- channels.telegram.accounts.magoo.groupPolicy
- channels.telegram.accounts.twain.groupPolicy
- channels.telegram.accounts.clinker.groupPolicy
- channels.telegram.accounts.carl.groupPolicy
- channels.telegram.accounts.canna.groupPolicy
- channels.telegram.accounts.maya.groupPolicy
- channels.telegram.accounts.grant.groupPolicy
- channels.telegram.accounts.selena.groupPolicy
With tools.elevated enabled, a prompt injection in those rooms can become a high-impact incident.

Set groupPolicy="allowlist" and keep elevated allowlists extremely tight.

Credentials dir is writable by others

/home/crogers2287/.openclaw/credentials mode=775; another user could drop/modify credential files.

Extension plugin tools may be reachable under permissive tool policy

Enabled extension plugins: email.
Permissive tool policy contexts:
- default
- agents.list.grant
- agents.list.maya
- agents.list.magoo
- agents.list.ghc-marketing
- agents.list.cannadeals-marketing
- agents.list.cardone
- agents.list.clinker
- agents.list.mark-twain
- agents.list.carl
- agents.list.selena
- agents.list.ceo

Use restrictive profiles (`minimal`/`coding`) or explicit tool allowlists that exclude plugin tools for agents handling untrusted input.

Telegram security warning

Telegram groups: groupPolicy="open" allows any member in allowed groups to trigger (mention-gated). Set channels.telegram.groupPolicy="allowlist" + channels.telegram.groupAllowFrom to restrict senders.

Telegram group commands have no sender allowlist

Telegram group access is enabled but no sender allowlist is configured; this allows any group member to invoke /… commands (including skill commands).

Approve yourself via pairing (recommended), or set channels.telegram.groupAllowFrom (or per-group groups.<id>.allowFrom).

Attack surface summary

No auth rate limiting configured

Gateway password is stored in config

Credentials dir is writable by others

Telegram security warning


**Observing Model Usage Patterns**

I've been analyzing the recent status updates and have identified that the `qwen3.5-35b-a3b` model is a commonly used model across multiple agent operations. This appears to be driving frustration. Digging deeper into the sessions data, the "defaults" section seems a good area to start.

exec

curl -s http://localhost:9069/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.5-35b-a3b","messages":[{"role":"user","content":"Say hello"}],"max_tokens":100}' | jq '.'


<think>
Thinking Process:

1.  **Analyze the Request:**
    *   Input: "Say hello"
    *   Intent: The user wants a greeting.
    *   Tone: Friendly, polite, simple.
    *   Constraints: None specific.

2.  **Determine the Response:**
    *   I need to acknowledge the request and provide a greeting.
    *   A simple "Hello!" or "Hi there!" is appropriate.


**Debugging the Configuration**

I'm frustrated; the model still outputs `<think>` tags, even with the "nothink" Jinja template and a supposedly correct chat template file defined. The `llama-swap` config now appears to be the culprit. The `--chat-template-file` argument may be incorrect in the command line used to launch the LLM server.


{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- else %}
{{- 'You are a helpful AI assistant that can use tools to help answer questions. When you need to use a tool, format your response as a tool call. If no tools are needed, respond directly.\n\n' }}
{%- endif %}
{{- '# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>' }}
{%- for tool in tools %}
{{- '\n' }}
{{- tool | tojson }}
{%- endfor %}
{{- '\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n' }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are a helpful AI assistant. Respond directly and concisely in English.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if message.role == 'user' %}
{{- '<|im_start|>user\n' + content + '<|im_end|>\n' }}
{%- elif message.role == 'assistant' and not loop.last %}
{{- '<|im_start|>assistant\n' + content + '<|im_end|>\n' }}
{%- elif message.role == 'tool' %}
{{- '<|im_start|>user\n<tool_response>\n' + content + '\n</tool_response><|im_end|>\n' }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n</think>\n\n' }}
{%- endif %}


**Analyzing the Qwen3 Prompt**

I've zeroed in on the critical Jinja code snippet in Qwen3. The conditional logic, specifically the part that forces the model to conclude its "thought" with `</think>` and then begin the response, is the key. I believe this directs the model's behavior effectively, and I need to work on how to adapt it to the other models.

write

{%- if tools %}
    {{- '<|im_start|>system\n' }}
    {%- if messages[0]['role'] == 'system' %}
        {{- messages[0]['content'] + '\n\n' }}
    {%- endif %}
    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\nYou are provided with function descriptions in XML format:\n<tools>\n" }}
    {%- for tool in tools %}
        {{- tool | tojson(indent=4) + '\n\n' }}
    {%- endfor %}
    {{- "</tools>\n\nFor each function call, output the function name and arguments in XML format:\n<function=\"name\">\n<parameter=\"name\">value</parameter>\n</function>" }}
    {{- '<|im_end|>\n' }}
{%- elif messages[0]['role'] == 'system' %}
    {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- endif %}
{%- for message in messages %}
    {%- if message['role'] == 'user' %}
        {{- '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }}
    {%- elif message['role'] == 'assistant' %}
        {%- if message.get('content') %}
            {{- '<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}
        {%- endif %}
    {%- elif message['role'] == 'tool' %}
        {{- '<|im_start|>tool\n<tool_response>\n' + message['content'] + '\n</tool_response>\n<|im_end|>\n' }}
    {%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
    {{- '<|im_start|>assistant\n</think>\n\n' }}
{%- endif %}


Successfully wrote 1366 bytes to /home/crogers2287/llama-swap/qwen35-nothink.jinja


exec

sudo systemctl restart llama-swap && sleep 2 && systemctl status llama-swap --no-pager | head -15


● llama-swap.service - llama-swap AI Model Proxy Server
     Loaded: loaded (/etc/systemd/system/llama-swap.service; enabled; preset: enabled)
     Active: active (running) since Wed 2026-02-25 16:42:42 UTC; 2s ago
       Docs: https://github.com/mostlygeek/llama-swap
    Process: 1284629 ExecStartPre=/bin/bash -c test -w /mnt/storage/ollama/gguf-models || (echo "ERROR: /mnt/storage/ollama/gguf-models is not writable" && exit 1) (code=exited, status=0/SUCCESS)
   Main PID: 1284642 (llama-swap-linu)
      Tasks: 13 (limit: 154421)
     Memory: 7.9M (peak: 9.6M)
        CPU: 102ms
     CGroup: /system.slice/llama-swap.service
             └─1284642 /home/crogers2287/llama-swap/build/llama-swap-linux-amd64 --config /home/crogers2287/llama-swap/config.yaml --listen 0.0.0.0:9069 --watch-config

Feb 25 16:42:42 fred llama-swap[1284642]: Watching Configuration for changes
Feb 25 16:42:42 fred llama-swap[1284642]: llama-swap listening on http://0.0.0.0:9069


Command still running (session oceanic-tidepool, pid 1286080). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! 👋 How can I help you today?"
      }
    }
  ],
  "created": 1772037821,
  "model": "Qwen3.5-35B-A3B-Q4_K_M.gguf",
  "system_fingerprint": "b8148-244641955",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 12,
    "prompt_tokens": 12,
    "total_tokens": 24
  },
  "id": "chatcmpl-gvJfyg96oJG62KlsmPjenDNT4MTFkoOu",
  "timings": {
    "cache_n": 0,
    "prompt_n": 12,
    "prompt_ms": 106.741,
    "prompt_per_token_ms": 8.895083333333334,
    "prompt_per_second": 112.42165615836464,
    "predicted_n": 12,
    "predicted_ms": 157.45,
    "predicted_per_token_ms": 13.120833333333332,
    "predicted_per_second": 76.21467132422991
  }
}

Process exited with code 0.

Hello! 👋 How can I help you today?


**Addressing TTS concerns**

I'm now focusing on the "TTS" aspect. Since the output is clean text, I'm theorizing it shouldn't trigger any fallback to text-to-speech. Perhaps, however, the model's response itself is the source of the problem.

grep -r "tts" /home/crogers2287/.openclaw/agents/clinker/*.jsonl 2>/dev/null | tail -20


(no output)


journalctl -u openclaw -n 100 --no-pager | grep -i tts


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)