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


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.


ENOENT: no such file or directory, access '/home/crogers2287/.openclaw/agents/clinker/WORKFLOW_AUTO.md'


ENOENT: no such file or directory, access '/home/crogers2287/.openclaw/agents/clinker/memory/2026-02-25.md'


- 🎯 **S1E5 "In Too Deep"** (aired today, Feb 11) - **search started** (priority)
- 📥 **S1E1-E4** (previously aired) - **search queued**
- ⏳ **S1E6 "Pain Auction"** (airs Feb 25) - monitored, will auto-grab when available

Sonarr is now searching indexers for all missing episodes. S1E5 should download first since it got priority search. Check your download client!
user: System: [2026-02-12 02:19:38 UTC] Exec completed (dawn-tid, code 0) :: Fear Factor: House of Fear (2026) - TVDB:463596 Way of Choices (2026) (2026) - TVDB:473485 Fear Factor Denmark (2025) - TVDB:471119 Pura Sangre (2026) (2026) - TVDB:463797 Disaster (2026) (2026) - TVDB:473732

[Telegram Skinny (@skinnyc813) id:6558388607

Source: memory/2026-02-13-credential-vault.md#L24-L36

user: System: [2026-02-22 07:25:28 UTC] Check /opt/agentmail/queue/ for new emails addressed to clinker. If there are unread emails (files you haven't processed), read them and respond appropriately. After processing, move the file to /opt/agentmail/processed/ to mark it as handled.

Conversation info (untrusted metadata):
```json
{
  "message_id": "1794",
  "sender": "6558388607"
}
```

Get with him and figure out what's going on. Fix it please.
user: System: [2026-02-22 07:25:28 UTC] Check /opt/agentmail/queue/ for new emails addressed to clinker. If there are unread emails (files you haven't processed), read them and respond appropriately. After processing, move the file to /opt/agentmail

Source: memory/2026-02-22-0725.md#L127-L160

# Session: 2026-02-22 07:25:58 UTC

- **Session Key**: agent:clinker:main
- **Session ID**: bfb69a97-b848-4249-98ac-d4681d08e390
- **Source**: telegram

## Conversation Summary

assistant: Pix is cooking. Here's the status:

**What's working:**
- ✅ Gateway WebSocket push (Fred → Pix) — real-time, confirmed
- ✅ ADB tools on Pixel — screenshots, taps, UI dumps all passing
- ✅ Reddit and LinkedIn tasks in progress
- ✅ Pix is actively updating you in Telegram

**Just fixed:**
- 🔧 The `send_directive_to_fred.sh` script — switched from WS to SSH transport since the gateway was rejecting plaintext WebSocket. SSH keys are now set up both ways.

**Comms architecture:**
- **Fred → Pix:** `openclaw ga

Source: memory/2026-02-22-0725.md#L1-L35

- 🎯 **S1E5 "In Too Deep"** (aired today, Feb 11) - **search started** (priority)
- 📥 **S1E1-E4** (previously aired) - **search queued**
- ⏳ **S1E6 "Pain Auction"** (airs Feb 25) - monitored, will auto-grab when available

Sonarr is now searching indexers for all missing episodes. S1E5 should download first since it got priority search. Check your download client!
user: System: [2026-02-12 02:19:38 UTC] Exec completed (dawn-tid, code 0) :: Fear Factor: House of Fear (2026) - TVDB:463596 Way of Choices (2026) (2026) - TVDB:473485 Fear Factor Denmark (2025) - TVDB:471119 Pura Sangre (2026) (2026) - TVDB:463797 Disaster (2026) (2026) - TVDB:473732

[Telegram Skinny (@skinnyc813) id:6558388607

Source: memory/dailies/2026-02-13-credential-vault.md#L24-L36

### Desktop2025 SSH Access (Added 2026-02-19)
- **Host:** desktop2025.taile5e8a3.ts.net (Tailscale)
- **IP:** 100.87.125.22
- **User:** croge
- **Auth:** SSH key ~/.ssh/id_rsa (RSA)
- **Status:** Windows 11, OpenSSH server, active
- **Usage:** `ssh -i ~/.ssh/id_rsa croge@desktop2025.taile5e8a3.ts.net`
- **SSH Config:**
  ```
  Host desktop2025 desktop2025.taile5e8a3.ts.net 100.87.125.22
      HostName desktop2025.taile5e8a3.ts.net
      User croge
      StrictHostKeyChecking no
      UserKnownHostsFile=/dev/null
  ```

### Spacebot Z.AI Endpoint Fix (2026-02-20)
- **Bug**: spacebot used `api.z.ai/api/paas/v4/` — Pro coding plan keys only work on `api.z.ai/api/coding/paas/v4/`
- **Fix**: one-

Source: MEMORY.md#L171-L204

qmd query 2026-02-25 --json -n 6 -c memory-root-clinker timed out after 8000ms


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?


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


still not really working Marcus Twain, [2/25/2026 10:36 AM] Model "local/qwej" is not allowed. Use /models to list providers, or /models <provider> to list models. Skinny, [2/25/2026 10:36 AM] Marcus Twain, [2/25/2026 10:36 AM] Model reset to default (qwen (local/qwen3.5-35b-a3b)). Skinny, [2/25/2026 10:36 AM] hello Skinny, [2/25/2026 10:38 AM] helllo Skinny, [2/25/2026 10:38 AM] /new Marcus Twain, [2/25/2026 10:39 AM] ✅ New session started · model: local/qwen3.5-35b-a3b Skinny, [2/25/2026 10:39 AM] /model qwen Marcus Twain, [2/25/2026 10:39 AM] What are you working on today, Chad? Skinny, [2/25/2026 10:39 AM] hello Marcus Twain, [2/25/2026 10:39 AM] What are you working on today, Chad? Marc

Source: memory/2026-02-25-1554.md#L69-L83

The reason it kept trying to use Nanbeige is that one of OpenClaw's internal tools (likely the memory search or a hidden fallback in the Marcus/Twain unit) had the word `nanbeige` hard-coded as its target ID.

### The Fix
1.  **Alias Hijack**: I've re-assigned the `nanbeige` alias to point directly to `local/qwen3.5-35b-a3b`.
2.  **Redirection**: Now, whenever OpenClaw or any tool asks for "Nanbeige", your system will transparently serve the fast **Qwen3.5-35B-A3B** model instead.
3.  **Legacy Renamed**: The actual Nanbeige model is still there but is now aliased to `nanbeige-legacy` so it won't be triggered accidentally.

**Current `openclaw models list` verified:**
*   `local/qwen3.5-35b-

Source: memory/2026-02-25-1554.md#L24-L45

user: System: [2026-02-25 15:42:05 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.

Conversation info (untrusted metadata):
```json
{
  "message_id": "2721",
  "sender": "6558388607"
}
```

still not really working Marcus Twain, [2/25/2026 10:36 AM] Model "local/qwej" is not allowed. Use /models to list providers, or /models <provider> to list models. Skinny, [2/25/2026 10:36 AM] Marcus Twain, [2/25/2026 10:36 AM] Model reset to default (qwen (local

Source: memory/2026-02-25-1554.md#L55-L69

# Session: 2026-02-25 15:54:34 UTC

- **Session Key**: agent:clinker:main:thread:12775
- **Session ID**: 7ce06b15-a368-4cdb-bf12-1e515a288e41
- **Source**: telegram

## Conversation Summary

user: System: [2026-02-25 15:35:20 UTC] Exec completed (ember-bi, code 0) :: memory.used [MiB] 2331 MiB 1807 MiB Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL cydonia-24b embeddinggemma-300m glm-4.7-flash glm47reap heretic nanbeige-reasoner nvidia-orchestrator-8b our…
System: [2026-02-25 15:36:32 UTC] Exec completed (mellow-b, code 0) :: s": 9, "total_tokens": 19 }, "id": "chatcmpl-SpIfdmzIYxuKnJEhz7KQbXBwCOtfUBsD", "timings": { "cache_n": 0, "prompt_n": 9, "prompt_ms": 105.423, "prompt_per_token_ms": 11.713666666…

Source: memory/2026-02-25-1554.md#L1-L27

still not really working Marcus Twain, [2/25/2026 10:36 AM] Model "local/qwej" is not allowed. Use /models to list providers, or /models <provider> to list models. Skinny, [2/25/2026 10:36 AM] Marcus Twain, [2/25/2026 10:36 AM] Model reset to default (qwen (local/qwen3.5-35b-a3b)). Skinny, [2/25/2026 10:36 AM] hello Skinny, [2/25/2026 10:38 AM] helllo Skinny, [2/25/2026 10:38 AM] /new Marcus Twain, [2/25/2026 10:39 AM] ✅ New session started · model: local/qwen3.5-35b-a3b Skinny, [2/25/2026 10:39 AM] /model qwen Marcus Twain, [2/25/2026 10:39 AM] What are you working on today, Chad? Skinny, [2/25/2026 10:39 AM] hello Marcus Twain, [2/25/2026 10:39 AM] What are you working on today, Chad? Marc

Source: memory/2026-02-25-1554.md#L84-L98

still not really working Marcus Twain, [2/25/2026 10:36 AM] Model "local/qwej" is not allowed. Use /models to list providers, or /models <provider> to list models. Skinny, [2/25/2026 10:36 AM] Marcus Twain, [2/25/2026 10:36 AM] Model reset to default (qwen (local/qwen3.5-35b-a3b)). Skinny, [2/25/2026 10:36 AM] hello Skinny, [2/25/2026 10:38 AM] helllo Skinny, [2/25/2026 10:38 AM] /new Marcus Twain, [2/25/2026 10:39 AM] ✅ New session started · model: local/qwen3.5-35b-a3b Skinny, [2/25/2026 10:39 AM] /model qwen Marcus Twain, [2/25/2026 10:39 AM] What are you working on today, Chad? Skinny, [2/25/2026 10:39 AM] hello Marcus Twain, [2/25/2026 10:39 AM] What are you working on today, Chad? Marc

Source: memory/2026-02-25-1554.md#L99-L113

qmd query local model TTS responding text to speech nanbeige qwen --json -n 6 -c memory-root-clinker timed out after 8000ms


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


# Session: 2026-02-21 21:10:38 UTC

- **Session Key**: agent:clinker:main
- **Session ID**: 0a981686-ea95-45ad-aca2-3569574500d0
- **Source**: telegram

## Conversation Summary

assistant: NO_REPLY
user: [Sat 2026-02-21 09:06 UTC] [System Message] [sessionId: c6ded207-a9bb-42c0-bece-377c2c7cbd69] A cron job "clinker-todo-check-4am" just completed successfully.

Result:
No TODO.md exists and no PROGRESS-LOG.md exists. TASKS.md shows two pending tasks:

1. **Task 2: Ouroboros orchestration end-to-end test** — Send a real message via @Ouro87_bot, confirm Nemotron primary (no fallback)
2. **Task 3: Spacebot streaming** — Write PR for SpacebotModel::stream() with SSE parsing

Both require either

Source: memory/2026-02-21-2110.md#L1-L31

- **Ouroboros e2e test** — needs a live Telegram message sent to @Ouro87_bot and verification. Can attempt this autonomously if desired.
- **Spacebot streaming PR** — blocked on upstream; no PR to work against yet.

No active work to resume. Going back to sleep.

Stats: runtime 19s • tokens 31.8k (in 31.0k / out 783)

A completed cron job is ready for user delivery. Convert the result above into your normal assistant voice and send that user-facing update now. Keep this internal context private (don't mention system/log/stats/session details or announce type), and do not copy the system message verbatim. Reply ONLY: NO_REPLY if this exact result was already delivered to the user in this same

Source: memory/2026-02-21-2110.md#L25-L46

- `pixel-health.sh` — tested on device, all green (battery 80%, ADB connected, gateway running)
   - `phone-monitor.sh` — tested from Fred, all passes
   - `adb-ui-helper.py` — Gemini vision-based element finder (solves Compose problem)
   - `social-poster.py` — high-level Reddit/LinkedIn/X posting with verification baked in
   - `send-screenshot.sh` — relay screenshots to Fred
   - Location: `/home/crogers2287/phone-agent-tools/`

2. ✅ **Research complete** — full report at `memory/research/android-automation-tools.md`:
   - **Best find: `reddit-mcp-server`** — can POST via Reddit API, no ADB needed at all
   - **PRAW** — standard Reddit Python lib, works in Termux, skips UI entirely

Source: memory/2026-02-22-email-check.md#L55-L73

- Waffleson's memory updated with Pix's details

**Pix is actively working on:**
- Reddit engagement in r/FLMedicalTrees
- LinkedIn blog sharing for CannDeals
- Plus the two new content pieces CannDeals just sent

All three agents (Clinker, CannDeals, Pix) are now aware of each other and know how to communicate.
assistant:

NO_REPLY
assistant:

NO_REPLY
user: System: [2026-02-22 07:18:28 UTC] Check /opt/agentmail/queue/ for new emails addressed to clinker. If there are unread emails (files you haven't processed), read them and respond appropriately. After processing, move the file to /opt/agentmail/processed/ to mark it as handled.

Conversation info (untrusted metadata):
```json
{
  "mess

Source: memory/2026-02-22-0725.md#L80-L115

**My recommendation:** Stop trying to make Pix tap buttons on screen. Install PRAW for Reddit and use the API directly — it's 100x more reliable than fighting Jetpack Compose UI. LinkedIn's API is trickier (unofficial only) but still better than ADB taps.

**Blocked tasks on my end:** Ouroboros E2E test and Spacebot streaming PR are both waiting on you — project dirs aren't on this host.

Want me to set up PRAW on the Pixel and get Reddit posting working via API?
assistant: NO_REPLY
user: System: [2026-02-22 12:52:28 UTC] Check /opt/agentmail/queue/ for new emails addressed to clinker. If there are unread emails (files you haven't processed), read them and respond appropriately. After proces

Source: memory/2026-02-22-email-check.md#L68-L89

still not really working Marcus Twain, [2/25/2026 10:36 AM] Model "local/qwej" is not allowed. Use /models to list providers, or /models <provider> to list models. Skinny, [2/25/2026 10:36 AM] Marcus Twain, [2/25/2026 10:36 AM] Model reset to default (qwen (local/qwen3.5-35b-a3b)). Skinny, [2/25/2026 10:36 AM] hello Skinny, [2/25/2026 10:38 AM] helllo Skinny, [2/25/2026 10:38 AM] /new Marcus Twain, [2/25/2026 10:39 AM] ✅ New session started · model: local/qwen3.5-35b-a3b Skinny, [2/25/2026 10:39 AM] /model qwen Marcus Twain, [2/25/2026 10:39 AM] What are you working on today, Chad? Skinny, [2/25/2026 10:39 AM] hello Marcus Twain, [2/25/2026 10:39 AM] What are you working on today, Chad? Marc

Source: memory/2026-02-25-1554.md#L114-L142

qmd query TTS only response empty content tool call NO_REPLY --json -n 6 -c memory-root-clinker timed out after 8000ms


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


im gonna work on this with a friend of mine, output a full rundown of what we are trying to accomplish and the issues we are running into, and what we have already tried
assistant: # VLAN Migration Project — Status Report

## Objective
Migrate from flat network (192.168.1.0/24) to segmented VLAN architecture:

| VLAN | Subnet | Purpose | SSIDs |
|------|--------|---------|-------|
| 10 | 192.168.10.0/24 | Management | - |
| 20 | 192.168.20.0/24 | Server/Lab | - |
| 30 | 192.168.30.0/24 | Business | Valhalla |
| 40 | 192.168.40.0/24 | IoT/Farm | AhoySmartHome, AhoyIOT, Ahoy2019-2G |
| 50 | 192.168.50.0/24 | Cameras | - |
| 60 | 192.168.60.0/24 | Guest | - |
| 99 | 192.168.99.0/24 | Quarantine

Source: memory/2026-02-23-0422.md#L271-L307

assistant: Excellent idea! I'll create a centralized credential database as a skill. Let me extract all the media server credentials from the nzb360 backup and structure them properly.
user: [Telegram Skinny (@skinnyc813) id:6558388607 +2m Thu 2026-02-12 02:22 UTC] Okay, and then I think we're still missing the latest 90 Day fiance before the 90 days episode as well
[message_id: 286]
assistant: Let me check Sonarr for 90 Day Fiancé: Before the 90 Days and find the latest episode.
assistant: I see **S8E9 "Initial Descent"** (aired Feb 1) shows it has a file. The next episode **S8E10 "Prepare For Arrival"** doesn't air until **Feb 15** (3 days from now).

Let me verify S8E9 actually downloaded

Source: memory/2026-02-13-credential-vault.md#L33-L52

1. **Task 2 — Ouroboros E2E test**: Needs to send a real message via @Ouro87_bot and confirm Nemotron primary (no fallback). The ouroboros project directory doesn't exist on this host anymore — can't proceed without knowing where it moved or getting direction from Chad.

2. **Task 3 — Spacebot streaming PR**: Needs `SpacebotModel::stream()` with SSE parsing. The spacebot project directory is also missing from this host. No upstream PR exists yet. Blocked.

**Decision: Sleep.** Both pending tasks depend on project directories/repos that aren't present on this machine and need Chad's input to locate or re-scope. Nothing actionable without human guidance. All other tracked work is complete.

St

Source: memory/2026-02-21-2110.md#L43-L55

# Session: 2026-02-23 17:48:58 UTC

- **Session Key**: agent:clinker:main
- **Session ID**: fbc10e3b-f740-4db9-a836-41511c8fc3f5
- **Source**: telegram

## Conversation Summary

assistant: Nemotron works. Let me do a full integration test:
assistant: **Full pipeline verified:**
- ✅ Local embeddings (embeddinggemma-300m)
- ✅ ChromaDB verb registry
- ✅ Semantic retrieval (correctly finds NASAGetAPOD)
- ✅ Code synthesis (Nemotron generates valid Python)

Now creating the skill:
assistant: **Done.** Skill created at `~/.openclaw/skills/web-verbs/`

**Verified working:**
- Local embeddings via llama-swap (embeddinggemma-300m)
- ChromaDB semantic verb retrieval
- Code synthesis with Nemotron
- F

Source: memory/2026-02-23-web-verbs.md#L1-L43

Read USER.md 2. Read learnings/LEARNINGS.md 3. Read memory/YYYY-MM-DD.md (today + yesterday) 4. Read MEMORY.md (main session only, never in groups) 5. Read PROTOCOL_COST_EFFICIENCY.md 6. Print: LOADED: USER | LEARNINGS | DAILY | MEMORY | PROTOCOL 2. The Write Discipline (Middle of file) After every task: 1. Log decision + outcome → memory/YYYY-MM-DD.md 2. If mistake → append to learnings/LEARNINGS.md 3. If significant context → update MEMORY.md (only during heartbeat reviews, never directly during tasks) 3. The Handover Protocol (Bottom of file - Fixes model-switch amnesia) Before session end or model switch: Write HANDOVER section to memory/YYYY-MM-DD.md: - What was discussed - What was dec

Source: memory/2026-02-23-web-verbs.md#L147-L149

qmd query reasoning_content output content assistant generation template --json -n 6 -c memory-root-clinker timed out after 8000ms


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.


- **Root cause:** Likely unmanaged switch stripping VLAN tags
- **Fix options:** Replace with managed switch OR use per-client AdGuard on VLAN 1

### Omada MCP Server (Added 2026-02-18)
- **Location:** /home/crogers2287/omada/omada-mcp/server.py
- **Type:** MCP (Model Context Protocol) server — 42 tools
- **Dependencies:** Custom omada-api wrapper at /home/crogers2287/omada/omada-api-wrapper
- **Capabilities:** Full Omada controller API — devices, APs, switches, gateways, clients, wireless, networks, firewall, monitoring, settings
- **Usage:** `mcporter call --stdio "venv/bin/python server.py" <tool_name> [args]`
- **Requires env vars:** OMADA_URL, OMADA_USERNAME (claude), OMADA_PASSWORD, OMADA_SITE_ID, OMADA_CONTROLLER_ID
- **Tested:** list_devices, get_ssids, get_dashboard all working
- **Reference:** Full tool list and SSID IDs in TOOLS.md

### Project Ouroboros v2 (Added 2026-02-19)
- **Location:** /home/crogers2287/ouroboros-v2/
- **Architecture:** Hybrid multi-model agent on Spacebot
- **Local model:** Nemotron 3 Nano 30B A3B Q6_K GGUF via llama-swap on port 9069
- **FP8 won't work:** RTX 3090 = compute cap 86, FP8 (modelopt) needs 89+ (Ada)
- **FP8 weights downloaded:** /mnt/storage/hf/huggingface/hub/models--nvidia--NVIDIA-Nemotron-3-Nano-30B-A3B-FP8/ (31GB, unusable on 3090s)
- **Routing:** Nemotron (local, $0) for conversation, GLM-5 (cloud) for coding/tools
- **Spacebot agent:** `ouroboros` in /mnt/storage/spacebot/data/config.toml
- **Telegram bot:** @Ouro87_bot (token: 8392787929:AAE...)
- **Telegram bridge:** /home/crogers2287/ouroboros-v2/telegram_bridge.py → systemd ouroboros-bridge.service
- **Personality:** Tony Stark / Iron Man energy
- **llama-swap symlink fix:** /mnt/storage/ollama/gguf-models/Nemotron...gguf → /mnt/storage/models/Nemotron...gguf
- **Port 9069:** llama-swap (NOT vLLM) — serves Nemotron + Orchestrator-8B + other models
- **Waffleson SOUL.md:** Rewritten to enforce loop-closing behavior

### Desktop2025 SSH Access (Added 2026-02-19)
- **Host:** desktop2025.taile5e8a3.ts.net (Tailscale)


Let me search for the Nanbeige fix section more specifically.

Question: nanbeige fix reasoning content template jinja nothink


### Spacebot Email Integration (2026-02-20)
- Waffleson: `waffleson@teamcfr.com`, queue `*_waffleson.json`
- Ouroboros: `ouro@teamcfr.com`, queue `*_ouro.json`
- Volume: `/opt/agentmail:/opt/agentmail:rw` mounted in docker-compose
- Email-check cron (300s) added directly to each agent's `spacebot.db`

### Nanbeige 4.1 3B Fix (Added 2026-02-23)
- **Problem:** Model outputs to `reasoning_content` instead of `content`, never finishes thinking
- **Solution:** Custom chat template that adds `</think>` at start of assistant generation
- **Template:** `/home/crogers2287/llama-swap/nanbeige-nothink.jinja`
- **Config:** `--chat-template-file /home/crogers2287/llama-swap/nanbeige-nothink.jinja`
- **Re

Source: MEMORY.md#L198-L233

# 2026-02-23

## [04:22] Nanbeige 4.1 3B Setup for Ouro

**Model**: Nanbeige/Nanbeige4.1-3B — 3B reasoning model, 256K native context
**GGUF**: `/mnt/storage/ollama/gguf-models/Nanbeige4.1-3B.Q6_K.gguf` (3.3GB, Q6_K)
**Speed**: ~164 tok/s on GPU 1

### Problems & Fixes

1. **Model split across GPUs** → Fixed with `--split-mode none --main-gpu 1`
2. **Reasoning stuck in reasoning_content** → Model outputs to `reasoning_content`, leaves `content` empty
3. **Custom template fix** → Add `</think>` at start of assistant generation to skip thinking phase
4. **Chinese hallucination** → Model is Chinese-first, needs English system prompt baked into template

### Final Template
`/home/crogers2287/lla

Source: memory/2026-02-23.md#L1-L42

The reason it kept trying to use Nanbeige is that one of OpenClaw's internal tools (likely the memory search or a hidden fallback in the Marcus/Twain unit) had the word `nanbeige` hard-coded as its target ID.

### The Fix
1.  **Alias Hijack**: I've re-assigned the `nanbeige` alias to point directly to `local/qwen3.5-35b-a3b`.
2.  **Redirection**: Now, whenever OpenClaw or any tool asks for "Nanbeige", your system will transparently serve the fast **Qwen3.5-35B-A3B** model instead.
3.  **Legacy Renamed**: The actual Nanbeige model is still there but is now aliased to `nanbeige-legacy` so it won't be triggered accidentally.

**Current `openclaw models list` verified:**
*   `local/qwen3.5-35b-

Source: memory/2026-02-25-1554.md#L24-L45

### Desktop2025 SSH Access (Added 2026-02-19)
- **Host:** desktop2025.taile5e8a3.ts.net (Tailscale)
- **IP:** 100.87.125.22
- **User:** croge
- **Auth:** SSH key ~/.ssh/id_rsa (RSA)
- **Status:** Windows 11, OpenSSH server, active
- **Usage:** `ssh -i ~/.ssh/id_rsa croge@desktop2025.taile5e8a3.ts.net`
- **SSH Config:**
  ```
  Host desktop2025 desktop2025.taile5e8a3.ts.net 100.87.125.22
      HostName desktop2025.taile5e8a3.ts.net
      User croge
      StrictHostKeyChecking no
      UserKnownHostsFile=/dev/null
  ```

### Spacebot Z.AI Endpoint Fix (2026-02-20)
- **Bug**: spacebot used `api.z.ai/api/paas/v4/` — Pro coding plan keys only work on `api.z.ai/api/coding/paas/v4/`
- **Fix**: one-

Source: MEMORY.md#L171-L204

qmd query nanbeige fix reasoning content template jinja nothink --json -n 6 -c memory-root-clinker timed out after 8000ms

# Session: 2026-02-25 15:54:34 UTC

- **Session Key**: agent:clinker:main:thread:12775
- **Session ID**: 7ce06b15-a368-4cdb-bf12-1e515a288e41
- **Source**: telegram

## Conversation Summary

user: System: [2026-02-25 15:35:20 UTC] Exec completed (ember-bi, code 0) :: memory.used [MiB] 2331 MiB 1807 MiB Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL cydonia-24b embeddinggemma-300m glm-4.7-flash glm47reap heretic nanbeige-reasoner nvidia-orchestrator-8b our…
System: [2026-02-25 15:36:32 UTC] Exec completed (mellow-b, code 0) :: s": 9, "total_tokens": 19 }, "id": "chatcmpl-SpIfdmzIYxuKnJEhz7KQbXBwCOtfUBsD", "timings": { "cache_n": 0, "prompt_n": 9, "prompt_ms": 105.423, "prompt_per_token_ms": 11.713666666…

Source: memory/2026-02-25-1554.md#L1-L27


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.


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


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


/home/crogers2287/llama-swap/config.yaml:            - qwen3.5-35b-a3b
/home/crogers2287/llama-swap/config.yaml:    qwen3.5-35b-a3b:


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


groups:
    main_models:
        exclusive: false
        members:
            - qwen3.5-35b-a3b
            - nanbeige-reasoner
            - Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL
        persistent: true
        swap: false
    ouroboros:
        exclusive: false
        members:
            - embeddinggemma-300m
            - heretic
            - qwen3.5-27b
            - qwen3-24b-heretic-neo
            - cydonia-24b
        persistent: true
        swap: false
    vision:
        exclusive: false
        members:
            - ouroboros-eye
            - qwen3.5-27b-vision
        persistent: true
        swap: false
macros:
    llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
models:
    qwen3.5-35b-a3b:
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        metadata:
            backend: llamacpp
            displayName: Qwen3.5 35B-A3B Q4_K_M (MoE, 131K, Vision)
            isVision: true
        proxy: http://localhost:${PORT}
    nanbeige-reasoner:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Nanbeige4.1-3B.Q6_K.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 32768 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --main-gpu 1 --split-mode none --host 0.0.0.0 --jinja --chat-template-file /home/crogers2287/llama-swap/nanbeige-nothink.jinja -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: Nanbeige 4.1 3B Q6_K (Reasoner)
        proxy: http://localhost:${PORT}
    Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 32768 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --main-gpu 0 --host 0.0.0.0 --jinja --no-mmap -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        metadata:
            backend: llamacpp
            displayName: Nemotron-3-Nano-30B-A3B-UD-Q6_K_XL
            isTemporary: false
        proxy: http://localhost:${PORT}
    embeddinggemma-300m:
        cmd: /home/crogers2287/llama-swap/embed-wrapper.sh --model /mnt/storage/ollama/gguf-models/embeddinggemma-300M-Q8_0.gguf --port ${PORT} --n-gpu-layers 0 --ctx-size 2048 --threads 8 --batch-size 128 --cont-batching --host 0.0.0.0 --embedding --pooling mean
        concurrencyLimit: 100
        metadata:
            backend: llamacpp
            displayName: EmbeddingGemma 300M Q8_0
            isEmbedding: true
        proxy: http://localhost:${PORT}
    heretic:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Llama3.3-8B-Instruct-Thinking-Heretic-Uncensored-Claude-4.5-Opus-High-Reasoning.Q6_K.gguf --port ${PORT} --n-gpu-layers 35 --ctx-size 32000 --threads 16 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja
        metadata:
            backend: llamacpp
            displayName: Llama3.3 8B Heretic (Uncensored Reasoning)
        proxy: http://localhost:${PORT}
    nvidia-orchestrator-8b:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/nvidia_Orchestrator-8B-Q8_0.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 2048 --threads 8 --batch-size 512 --flash-attn on --cont-batching --main-gpu 0 --host 0.0.0.0 --jinja --no-mmap -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: NVIDIA Orchestrator 8B (Task Planning)
            isTemporary: false
        proxy: http://localhost:${PORT}
    ouroboros-eye:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Qwen3-VL-2B-Instruct-1M-Q8_0.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 4096 --threads 4 --batch-size 256 --flash-attn on --cont-batching --main-gpu 1 --host 0.0.0.0 --jinja --mmproj /mnt/storage/ollama/gguf-models/mmproj-Qwen3VL-2B-Instruct-Q8_0.gguf -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: Ouroboros Eye (Qwen3-VL-2B Vision)
            isVision: true
        proxy: http://localhost:${PORT}
    glm-4.7-flash:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/GLM-4.7-Flash-Q6_K.gguf --port ${PORT} --n-gpu-layers 999 --ctx-size 65536 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja --split-mode layer --tensor-split 0.5,0.5 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        metadata:
            backend: llamacpp
            displayName: GLM-4.7-Flash Q6_K (64K Context)
        proxy: http://localhost:${PORT}
    glm47reap:
        cmd: ${llama-server} --model /home/crogers2287/models/GLM-4.7-Flash-REAP-23B-A3B-Q6_K.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 131072 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja --split-mode layer --tensor-split 0.5,0.5 -ctk q8_0 -ctv q8_0 --reasoning-budget 0
        metadata:
            backend: llamacpp
            displayName: GLM-4.7-Flash REAP 23B-A3B Q6_K (128K)
        proxy: http://localhost:${PORT}
    qwen3-24b-heretic-neo:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/Qwen3-24B-A4B-Freedom-Think-Ablit-Heretic-Neo-D_AU-Q6_K-imat.gguf --port ${PORT} --n-gpu-layers 100 --ctx-size 16384 --threads 8 --batch-size 1024 --flash-attn on --cont-batching --host 0.0.0.0 --jinja -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: Qwen3 24B Heretic NEO (Uncensored Reasoning)
        proxy: http://localhost:${PORT}
    cydonia-24b:
        cmd: ${llama-server} --model /mnt/storage/ollama/gguf-models/TheDrummer_Cydonia-24B-v4.1-Q6_K_L.gguf --port ${PORT} --n-gpu-layers 999 --ctx-size 32768 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja --split-mode layer --tensor-split 0.5,0.5 -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: Cydonia 24B v4.1 Q6_K_L (32K)
        proxy: http://localhost:${PORT}
    qwen3.5-27b:
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-27B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 999 --ctx-size 65536 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja --split-mode layer --tensor-split 0.5,0.5 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        metadata:
            backend: llamacpp
            displayName: Qwen3.5 27B Q4_K_M (64K Context)
            isVision: false
        proxy: http://localhost:${PORT}
    qwen3.5-27b-vision:
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-27B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 999 --ctx-size 32768 --threads 8 --batch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --jinja --mmproj /mnt/storage/gguf-models/Qwen3.5-27B-mmproj-F16.gguf --split-mode layer --tensor-split 0.5,0.5 -ctk q8_0 -ctv q8_0
        metadata:
            backend: llamacpp
            displayName: Qwen3.5 27B Vision Q4_K_M (32K)
            isVision: true
        proxy: http://localhost:${PORT}


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


Successfully replaced text in /home/crogers2287/llama-swap/config.yaml.

     ...
  27 macros:
  28     llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
  29 models:
  30     qwen3.5-35b-a3b:
- 31         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
+ 31         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
  32         metadata:
  33             backend: llamacpp
  34             displayName: Qwen3.5 35B-A3B Q4_K_M (MoE, 131K, Vision)
  35             isVision: true
     ...


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


● 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 15:35:06 UTC; 32min ago
       Docs: https://github.com/mostlygeek/llama-swap
    Process: 353043 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: 353064 (llama-swap-linu)
      Tasks: 108 (limit: 154421)
     Memory: 6.4G (peak: 6.8G)
        CPU: 32min 28.445s
     CGroup: /system.slice/llama-swap.service
             ├─353064 /home/crogers2287/llama-swap/build/llama-swap-linux-amd64 --config /home/crogers2287/llama-swap/config.yaml --listen 0.0.0.0:9069 --watch-config
             └─359835 /mnt/storage/llama.cpp-nemotron/build/bin/llama-server --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port 5812 --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

Feb 25 16:07:21 fred llama-swap[353064]: slot update_slots: id  0 | task 16109 | prompt processing progress, n_tokens = 86016, batch.n_tokens = 2048, progress = 0.814229
Feb 25 16:07:21 fred llama-swap[353064]: srv  log_server_r: done request:   127.0.0.1 400
Feb 25 16:07:21 fred llama-swap[353064]: srv  log_server_r: done request: HEAD / 127.0.0.1 200
Feb 25 16:07:21 fred llama-swap[353064]: srv  log_server_r: done request: GET / 127.0.0.1 200
Feb 25 16:07:22 fred llama-swap[353064]: slot update_slots: id  0 | task 16109 | n_tokens = 86016, memory_seq_rm [86016, end)
Feb 25 16:07:22 fred llama-swap[353064]: slot update_slots: id  0 | task 16109 | prompt processing progress, n_tokens = 88064, batch.n_tokens = 2048, progress = 0.833616
Feb 25 16:07:23 fred llama-swap[353064]: slot update_slots: id  0 | task 16109 | n_tokens = 88064, memory_seq_rm [88064, end)
Feb 25 16:07:23 fred llama-swap[353064]: slot update_slots: id  0 | task 16109 | prompt processing progress, n_tokens = 90112, batch.n_tokens = 2048, progress = 0.853002
Feb 25 16:07:25 fred llama-swap[353064]: slot update_slots: id  0 | task 16109 | n_tokens = 90112, memory_seq_rm [90112, end)
Feb 25 16:07:25 fred llama-swap[353064]: slot update_slots: id  0 | task 16109 | prompt processing progress, n_tokens = 92160, batch.n_tokens = 2048, progress = 0.872389


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


Command still running (session quick-cloud, pid 798159). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


The restart is running. Let me wait for it.


● llama-swap.service - llama-swap AI Model Proxy Server
     Loaded: loaded (/etc/systemd/system/llama-swap.service; enabled; preset: enabled)
     Active: activating (auto-restart) (Result: exit-code) since Wed 2026-02-25 16:07:45 UTC; 2s ago
       Docs: https://github.com/mostlygeek/llama-swap
    Process: 800778 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)
    Process: 800799 ExecStart=/home/crogers2287/llama-swap/build/llama-swap-linux-amd64 --config /home/crogers2287/llama-swap/config.yaml --listen 0.0.0.0:9069 --watch-config (code=exited, status=1/FAILURE)
   Main PID: 800799 (code=exited, status=1/FAILURE)
        CPU: 90ms

Process exited with code 3.


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


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.

exec

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


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 16:07:44 fred llama-swap[353064]: [WARN] metrics skipped, HTTP status=502, path=/v1/chat/completions
Feb 25 16:07:44 fred llama-swap[353064]: [INFO] Request 127.0.0.1 "POST /v1/chat/completions HTTP/1.1" 502 -1 "OpenAI/JS 6.10.0" 1m9.062602599s
Feb 25 16:07:45 fred llama-swap[353064]: 2026/02/25 16:07:45 http: proxy error: dial tcp 127.0.0.1:5812: connect: connection refused
Feb 25 16:07:45 fred llama-swap[353064]: [WARN] metrics skipped, HTTP status=502, path=/v1/chat/completions
Feb 25 16:07:45 fred llama-swap[353064]: [INFO] Request 127.0.0.1 "POST /v1/chat/completions HTTP/1.1" 502 -1 "OpenAI/JS 6.10.0" 1.463557ms
Feb 25 16:07:45 fred llama-swap[353064]: 2026/02/25 16:07:45 http: proxy error: dial tcp 127.0.0.1:5812: connect: connection refused
Feb 25 16:07:45 fred llama-swap[353064]: [WARN] metrics skipped, HTTP status=502, path=/v1/chat/completions
Feb 25 16:07:45 fred llama-swap[353064]: [INFO] Request 127.0.0.1 "POST /v1/chat/completions HTTP/1.1" 502 -1 "OpenAI/JS 6.10.0" 1.601486ms
Feb 25 16:07:45 fred llama-swap[353064]: [WARN] <qwen3.5-35b-a3b> ExitError >> signal: killed, exit code: -1
Feb 25 16:07:45 fred llama-swap[353064]: [INFO] <qwen3.5-35b-a3b> process exited but not StateStopping, current state: ready
Feb 25 16:07:45 fred llama-swap[353064]: [INFO] Request 100.101.216.76 "GET /api/events HTTP/1.1" 200 1302998 "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36" 32m38.911704602s
Feb 25 16:07:45 fred llama-swap[353064]: Server shutdown error: context deadline exceeded
Feb 25 16:07:45 fred llama-swap[800799]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:07:50 fred llama-swap[802085]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:07:55 fred llama-swap[803018]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:01 fred llama-swap[804160]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:06 fred llama-swap[805616]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:11 fred llama-swap[807064]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:16 fred llama-swap[808311]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:21 fred llama-swap[809416]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:26 fred llama-swap[810655]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:32 fred llama-swap[811696]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:37 fred llama-swap[812906]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:42 fred llama-swap[814148]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:47 fred llama-swap[815401]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:52 fred llama-swap[816665]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:08:57 fred llama-swap[817793]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:09:03 fred llama-swap[818809]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:09:08 fred llama-swap[820075]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:09:13 fred llama-swap[821485]: Error loading config: yaml: line 31: mapping values are not allowed in this context


SECURITY NOTICE: The following content is from an EXTERNAL, UNTRUSTED source (e.g., email, webhook).
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<<<EXTERNAL_UNTRUSTED_CONTENT>>>
Source: Web Fetch
---
# llama.cpp/tools/llama-bench

Performance testing tool for llama.cpp.

## Table of contents

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

## Syntax

```
usage: llama-bench [options]

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

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

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

llama-bench can perform three types of tests:

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

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

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

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

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

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

## Examples

### Text generation with different models

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

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

### Prompt processing with different batch sizes

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

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

### Different numbers of threads

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

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

### Different numbers of layers offloaded to the GPU

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

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

### Different prefilled context

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

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

## Output formats

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

### Markdown

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

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

### CSV

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

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

### JSON

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

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

### JSONL

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

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

### SQL

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

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

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

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

<<<END_EXTERNAL_UNTRUSTED_CONTENT>>>


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:


macros:
    llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
models:
    qwen3.5-35b-a3b:
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --chat-template-kwargs "{\"enable_thinking\": false}" --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none
        metadata:
            backend: llamacpp
            displayName: Qwen3.5 35B-A3B Q4_K_M (MoE, 131K, Vision)
            isVision: true
        proxy: http://localhost:${PORT}

[80 more lines in file. Use offset=37 to continue.]


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.


     ...
  27 macros:
  28     llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
  29 models:
  30     qwen3.5-35b-a3b:
- 31         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
+ 31         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
  32         metadata:
  33             backend: llamacpp
  34             displayName: Qwen3.5 35B-A3B Q4_K_M (MoE, 131K, Vision)
  35             isVision: true
     ...


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


● llama-swap.service - llama-swap AI Model Proxy Server
     Loaded: loaded (/etc/systemd/system/llama-swap.service; enabled; preset: enabled)
     Active: activating (auto-restart) (Result: exit-code) since Wed 2026-02-25 16:11:18 UTC; 3s ago
       Docs: https://github.com/mostlygeek/llama-swap
    Process: 849793 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)
    Process: 849795 ExecStart=/home/crogers2287/llama-swap/build/llama-swap-linux-amd64 --config /home/crogers2287/llama-swap/config.yaml --listen 0.0.0.0:9069 --watch-config (code=exited, status=1/FAILURE)
   Main PID: 849795 (code=exited, status=1/FAILURE)
        CPU: 82ms

(Command exited with code 3)


Still failing. Let me check the specific error.

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


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 16:11:28 fred llama-swap[852064]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:11:33 fred llama-swap[853098]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:11:38 fred llama-swap[854240]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:11:43 fred llama-swap[855397]: Error loading config: yaml: line 31: mapping values are not allowed in this context
Feb 25 16:11:49 fred llama-swap[856683]: Error loading config: yaml: line 31: mapping values are not allowed in this context


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


llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server$
models:$
    qwen3.5-35b-a3b:$
        cmd: ${llama-server} --model /mnt/storage/gguf-models/Qwen3.5-35B-A3B-Q4_K_M.gguf --port ${PORT} --n-gpu-layers 99 --ctx-size 131072 --threads 12 --batch-size 2048 --ubatch-size 2048 --flash-attn on --cont-batching --host 0.0.0.0 --chat-template chatml --chat-template-kwargs '{"enable_thinking": false}' --mmproj /mnt/storage/gguf-models/Qwen3.5-35B-A3B-mmproj-F16.gguf --split-mode layer --tensor-split 1,1 -ctk q8_0 -ctv q8_0 --reasoning-budget 0 --reasoning-format none$
        metadata:$
            backend: llamacpp$
            displayName: Qwen3.5 35B-A3B Q4_K_M (MoE, 131K, Vision)$
            isVision: true$


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:


     ...
  27 macros:
  28     llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
  29 models:
  30     qwen3.5-35b-a3b:
- 31         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
+ 31         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
  32         metadata:
  33             backend: llamacpp
  34             displayName: Qwen3.5 35B-A3B Q4_K_M (MoE, 131K, Vision)
  35             isVision: true
     ...


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


(no output)


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


● 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 16:12:40 UTC; 3s ago
       Docs: https://github.com/mostlygeek/llama-swap
    Process: 868133 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: 868139 (llama-swap-linu)
      Tasks: 13 (limit: 154421)
     Memory: 8.0M (peak: 9.6M)
        CPU: 111ms
     CGroup: /system.slice/llama-swap.service
             └─868139 /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 16:12:40 fred llama-swap[868139]: Watching Configuration for changes
Feb 25 16:12:40 fred llama-swap[868139]: llama-swap listening on http://0.0.0.0:9069
Feb 25 16:12:41 fred llama-swap[868139]: [INFO] Request 100.101.216.76 "GET /api/version HTTP/1.1" 200 58 "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36" 80.289µs
Feb 25 16:12:41 fred llama-swap[868139]: [INFO] Request 100.101.216.76 "GET /api/groups HTTP/1.1" 200 568 "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/144.0.0.0 Safari/537.36" 202.348µs


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


Command still running (session keen-fjord, pid 878849). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


<think>
Thinking Process:

1.  **Analyze the Request:**
    *   Input: "Say hello"

Process exited with code 0.


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 '.'


<think>
Thinking Process:

1.  **Analyze the Request:** The user is saying "Say hello". This is a simple, direct greeting.
2.  **Determine the appropriate response:** A friendly, polite greeting in return is expected.
3.  **Draft the response:** "Hello!", "Hi there!", "Greetings!", etc.
4.  **Select the best option:** "Hello! How can I help you today?" is friendly and opens the door for


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


429 {"type":"error","error":{"type":"rate_limit_error","message":"This request would exceed your account's rate limit. Please try again later."},"request_id":"req_011CYV5w3Yx74UjnMHH4qRgN"}


429 {"type":"error","error":{"type":"rate_limit_error","message":"This request would exceed your account's rate limit. Please try again later."},"request_id":"req_011CYV5wQdiB93NPjj2QANJ9"}


429 {"type":"error","error":{"type":"rate_limit_error","message":"This request would exceed your account's rate limit. Please try again later."},"request_id":"req_011CYV5wvkCYMn5tz7FneoxS"}


429 {"type":"error","error":{"type":"rate_limit_error","message":"This request would exceed your account's rate limit. Please try again later."},"request_id":"req_011CYV5xhdCLj9wHwxkpUP2k"}


429 {"type":"error","error":{"type":"rate_limit_error","message":"This request would exceed your account's rate limit. Please try again later."},"request_id":"req_011CYV5xsHNUKKRw3fpLDjpM"}


429 {"type":"error","error":{"type":"rate_limit_error","message":"This request would exceed your account's rate limit. Please try again later."},"request_id":"req_011CYV5yC5iD4SJdctzUJ9NG"}


429 {"type":"error","error":{"type":"rate_limit_error","message":"This request would exceed your account's rate limit. Please try again later."},"request_id":"req_011CYV5yfVV5JfTWcrBeFDgU"}


429 {"type":"error","error":{"type":"rate_limit_error","message":"This request would exceed your account's rate limit. Please try again later."},"request_id":"req_011CYV5zTyDT7LxNLyahCssf"}


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


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


ggml_cuda_init: found 2 CUDA devices:
  Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes
  Device 1: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes


(no output)


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


-rwxrwxrwx 1 root root 144053464 Feb 25 02:14 /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
2026-02-25 02:14:43.312395500 +0000


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


(Command exited with code 1)


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"


No TTS config in gateway.yaml


No tts in openclaw config


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"


No TTS in SOUL.md


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"


Created qwen35-nothink.jinja


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


Successfully replaced text in /home/crogers2287/llama-swap/config.yaml.

     ...
  27 macros:
  28     llama-server: /mnt/storage/llama.cpp-nemotron/build/bin/llama-server
  29 models:
  30     qwen3.5-35b-a3b:
- 31         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
+ 31         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
  32         metadata:
  33             backend: llamacpp
  34             displayName: Qwen3.5 35B-A3B Q4_K_M (MoE, 131K, Vision)
  35             isVision: true
     ...


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


Command aborted by signal SIGTERM


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

still tts


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


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


**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