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turbofit

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更新时间2026年7月4日 11:23

Opinionated unified LLM backend (turbofit v1.1). Picks the best main + aux model for your hardware — local or API — launches them detached, wires Hermes-Agent config, and adapts to live VRAM pressure via a scaling ladder. Three hardware tiers: Beefy (local+local), Modest (API+local), Thin (API+API). `serve auto main` auto-detects GPU and suggests the right setup. API fallback is always available (free: DeepSeek V4 Pro + Kimi K2.6). Replaces llama-launch, omni-va, and ad-hoc llama-server scripts. Catalog schema supports per-model binary pinning (atomic fork vs stock), named flag presets (nextn, draft-mtp, turbo4-kv, vision-mmproj), tier ladder (s/sf/sd/f/c), and the 64K Hermes context floor is enforced everywhere. End-user UX is `serve auto main` and the user is done.

安装

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来源

SouthpawIN

SouthpawIN/sovth-config

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在 Manus 中运行

Turbofit v5 — opinionated unified backend

End-user UX is serve auto main — picks the best main model for your system state, launches it detached, and wires Hermes config. The user is done.

For the scaling/adaptation half of the opinionated story, see references/scaling-ladder.md.

When to use

Trigger phrases: "set up my local LLM", "launch a model", "what model should I run", "my GPU is busy, scale down", "I need a coder model", "I need vision", "turbofit auto", "swap models", "swap main", "swap aux", "which model fits my box", "stop everything".

Quick start

# Source the shim (one-time setup)
source <hermes-skills-dir>/turbofit/scripts/turbofit.sharco

# One-shot: pick best main for your box, launch, wire Hermes
serve auto main                    # opinionated: auto-detects hardware, picks local or API
serve auto main --vision           # require vision
serve auto main --api              # force API mode (no local GPU needed)
serve auto main --free             # only free API endpoints
AUTO_CTX=131072 serve auto main    # raise ctx target

# Stop everything
serve stop-all

# Browse the catalog (featured first, tier-ordered)
serve catalog

# Launch a specific model
serve darwin-28b-reason
serve string darwin-apex-36b-i-compact   # print launch string, don't launch

# Register a new model
name mymodel /path/to/file.gguf --port 9090

# Wire to Hermes (main / aux)
serve main mymodel --ui tui
serve aux mymodel --ui tui

# VRAM probe (live)
serve vram

# Adapt to current VRAM pressure
serve downscale

Dynamic Model Database

The model universe, pricing, and benchmarks are stored in references/model-database.yaml — a single source of truth that is auto-updated daily by a research cron job.

How it works

Daily research cron runs at 6am — scans OpenRouter, HuggingFace, Price Per Token, and LLMCheck for new models, pricing changes, and benchmark updates
Research script (scripts/research-models.py) fetches live data and generates a report
Agent reviews the report, updates model-database.yaml with new models or pricing changes
GitHub sync (scripts/sync-github.sh) pushes updates to SouthpawIN/turbofit (primary) and SouthpawIN/sovth-config (collection)
All turbofit users get fresh data by running hermes skills update turbofit

What's in the database

Each model entry includes:

Pricing across all providers (Nous, OpenRouter, NIM, direct API)
Context window and supported context tiers
Vision capability (text-only models must pair with vision aux)
Benchmark scores (MMLU, SWE-Verified, HumanEval, AIME — when available)
Local model info (GGUF repo, quants, size, archetypes, mmproj)
Discovery date and last verified date

Manual usage

# Run the research script manually
python3 ~/.hermes/skills/turbofit/scripts/research-models.py

# Check the latest report
cat ~/.hermes/skills/turbofit/references/research-report.md

# Sync to GitHub manually (pushes to SouthpawIN/turbofit + SouthpawIN/sovth-config)
bash ~/.hermes/skills/turbofit/scripts/sync-github.sh

# Update turbofit skill (for end users)
hermes skills update turbofit

Opinionated defaults (turbofit v5)

Setting	Value	Source
ctx floor	65536 tokens	Hermes-Agent hard requirement
tok/s floor (main)	25 tok/s	Spec decoding assumption
prefer vision on main	yes	Use `vision:` field in catalog
tier ladder	`s → sf → sd → f → c`	`serve auto`
scale down triggers	free VRAM < 14GB → < 8GB → < 4GB	`serve downscale`
per-model binary pin	atomic (TurboQuant+NextN) or stock	`binary:` field

End-user UX: serve auto main → done. If VRAM pressure hits, serve downscale adapts.

Catalog schema (v5 extended)

models:
  <alias>:
    # Required
    launcher: llama-cpp           # llama-cpp | ollama | vllm | sglang
    path: /abs/path/to.gguf        # OR HF repo for vllm/sglang
    port: 11500                    # auto-assigned if absent

    # Recommended
    ctx: 262144                    # 64K floor enforced
    gpu: 0                         # 0 | 1 (single-GPU target)
    gpu: 0                         # 0 | 1 (single-GPU target)
    mmproj: /path/to/mmproj.gguf   # vision projector
    presets: [nextn, turbo4-kv, no-mmap]   # see below
    extra_args: [--draft-block-size 3]           # raw flag list
    aliases: [short, alt]          # alternative names resolved by serve的政策
    description: "..."             # shown in `serve catalog`
    tags: [qwen, mtp, featured]

    # Opinionated metadata (used by `serve auto` and Garage UI)
    tier: s                        # s | sf | sd | f | c
    featured: true                 # Garage top row
    tok_s_target: 107              # measured throughput
    vision: true                   # has vision tower + mmproj
    size_gb: 16.0                  # disk footprint
    hf_repo: org/repo              # for `serve fetch <alias>` (future)
    role: main                     # main | aux | either

Named flag presets

Apply by listing in presets:. Multiple presets merge; later presets override earlier flags.

Preset	Expands to
`nextn`	`--spec-type nextn --draft-block-size 3`
`nextn-tight`	`--spec-type nextn --draft-block-size 2`
`draft-mtp`	`--spec-type draft-mtp`
`draft-mtp-tight`	`--spec-type draft-mtp --draft-block-size 2`
`turbo4-kv`	`-ctk turbo4 -ctv turbo4`
`turbo3-kv`	`-ctk turbo3 -ctv turbo3`
`turbo2-kv`	`-ctk turbo2 -ctv turbo2`
`q8-kv`	`-ctk q8_0 -ctv q8_0`
`q4-kv`	`-ctk q4_0 -ctv q4_0`
`no-mmap`	`--no-mmap`
`split-none`	`--split-mode none`
`mlock`	`--mlock`
`cpu-moe-2` / `cpu-moe-4` / `cpu-moe-8`	`--n-cpu-moe N` (MoE expert offload)
`parallel-4` / `parallel-2` / `parallel-1`	`--parallel N`

Multi-GPU tensor split: use extra_args: ['--tensor-split', 'X,Y'] in catalog — no hardcoded preset.

Tier ladder (used by `serve auto`)

Tier	Meaning	Examples
`s`	smartest	Darwin Reason, Darwin Apex-Compact, Prism Eagle
`sf`	smart + fast	Carwin-MTP, Qwopus v2-MTP, Qwopus Coder-MTP
`sd`	smart + dense	Carnice Apex Compact
`f`	fast	Qwable MTP, Qwopus abliterated-MTP
`c`	cheap	Qwen legacy, devstral, step-flash, omni-3b

Commands

# Install / update
serve install                            # llama.cpp from source (atomic fork if path set)
serve install <launcher>                 # one launcher: llama-cpp, ollama, vllm, sglang
serve update                             # update llama.cpp
serve update <launcher|all>              # specific launcher or all
serve check                              # version status

# Hardware / VRAM
serve fit <model> [ctx]                  # llmfit fit check (default ctx=65536)
serve vram                               # live GPU VRAM probe (JSON)
serve recommend                          # scan catalog, rank by fit (ctx≥64K, tok/s≥25, Q4, vision)

# Catalog
serve register <alias> <path>            # register model
           [--launcher llama-cpp|ollama|vllm|sglang] [--port N]
serve catalog                            # show registered (featured first, tier-ordered)

# Launch
serve <alias>                            # launch detached, shows backend/port/logs
serve string <alias>                     # print launch string, don't launch
serve stop <alias>                       # stop a running server
serve stop-all                           # stop everything
serve list                               # list running + detect rogue llama-servers

# Fetch / benchmark
serve fetch <alias>                      # download missing model from HF (uses hf_repo)
serve bench <alias>                      # lm-eval-harness benchmark (launches if needed)

# Opinionated auto (turbofit v1.1 — hardware-aware, API-aware)
serve auto main [--vision] [--api] [--free] [--ui ...]    # pick best main (auto-detects hardware, picks local or API)
serve auto aux [--vision] [--api] [--free] [--ui ...]     # pick best aux
serve downscale                          # adapt to current VRAM pressure
AUTO_CTX=131072 serve auto main          # override ctx target

# Hardware-aware auto-detection:
#   ≥24GB VRAM → local main + local aux (Beefy)
#   8-24GB VRAM → API main + free/cheap aux (Modest)
#   <8GB / no GPU → API main + API aux, zero cost with free endpoints (Thin)
# Use --api to force API mode, --free to restrict to free endpoints
#
# API pairings use the model universe from references/api-pairing-matrix.md
# Gateway indicators: 🟢 NOUS+TG (full Tool Gateway), 🟡 NOUS+OR, 🟠 NOUS+NIM, ⚪ NIM (free, no TG)

# Hermes routing
serve main <alias> [--ui tui|dashboard|gateway|desktop|herm]
serve aux  <alias> [--ui ...]
serve herm <alias>                       # launch + main + herm TUI (herm handles hermes internally)
serve herm aux <alias>
serve herm                               # auto-pick main + launch herm TUI

# NVIDIA NIM API (curated)
serve api list
serve api use <rank|api_id> [main|aux]

How the auto-picker decides

Filter catalog by role (main | aux) and (if --vision) vision: true.
Filter by ctx >= target (default 65536).
Sort by (tier_rank, featured, -tok_s_target) — best tier wins, then featured, then speed.
Skip entries whose path: doesn't exist (catalog has them but disk doesn't).
Launch via the per-model binary: if set, else stock llama-server.

The auto-picker does NOT yet weight by current VRAM headroom. Use serve downscale to adapt after the fact, or run serve vram first to know.

How the scaling ladder works (v1.1 — 3 hardware tiers, universal)

The ladder covers three hardware profiles and is hardware-neutral — any user can plug in their GPU and get the right setup. See references/scaling-ladder.md for full step-by-step details.

Hardware Tiers (auto-detected by `serve auto`)

Tier	VRAM	Typical GPUs	Default Main	Default Aux
Beefy	≥24GB	Dual GPU or single 24GB+	Local 27-28B dense (Q4)	Local 35B MoE 3B-active
Modest	8-24GB	RTX 3060/4060/4070 (8-16GB)	DeepSeek V4 Pro (API)	Qwen 3.6 Plus (OR free) or MiniMax M3 (NIM free)
Thin	<8GB or no GPU	Integrated graphics, no GPU	DeepSeek V4 Flash (free NIM)	MiniMax M3 (free NIM)

serve auto detects which tier you're in by probing nvidia-smi. If no NVIDIA GPU is found, it defaults to Thin (API-only). Use --api to force API mode, --free to restrict to free endpoints.

Context-Level Ladder (4 tiers)

Level	Context	Local VRAM (Beefy)	API Price Tier
1	1M	~40GB+ (dual GPU)	Free-Budget
2	512K	~28GB (pressured)	Budget-Mid
3	262K	~16GB (single GPU)	Mid-Premium
4	132K	~8GB (survival)	Budget

Beefy-tier scaling ladder (7 steps, triggered by `serve downscale`)

Uses model archetypes — users register their own local models that match the archetype. serve recommend scans the catalog and picks the best fit.

Step  1: Ideal          — 27-28B dense (Q4) + 35B MoE (3B active) aux, both @ 1M
Step  2: Mild pressure  — Offload aux MoE experts to CPU (--cpu-moe), ~10 tok/s
Step  3: Moderate       — Drop both models' context to 512K
Step  4: High pressure  — Drop local aux, route aux to API (free vision model)
Step  5: Swap main       — Swap to 27B hybrid/Mamba (lighter, ~14 GB)
Step  6: MoE main        — Swap to 35B MoE 3B-active main + API aux @ 132K
Step  7: API-only       — No local serving viable. API main + API aux. Zero cost with free endpoints.

Each step preserves maximum intelligence while respecting VRAM. Never auto-skip steps based on free VRAM alone — present the ladder and let users choose, or use serve downscale which walks it conservatively.

API Pairing Matrix

For API-only users (Modest/Thin tiers) or Beefy API fallback, see references/api-pairing-matrix.md for the complete pairing matrix — all model combinations across 4 context tiers and 5 price tiers, with Provider Gateway indicators showing whether the Nous Tool Gateway is active.

Gateway indicators:

🟢 NOUS — Both through Nous
🟡 NOUS+OR — Main through Nous, aux through OpenRouter (10% bonus)
🟠 NOUS+NIM — Main through Nous, aux through NIM (free)
⚪ NIM — Both through NIM (free)

Note: The Nous Tool Gateway (Firecrawl, FAL, OpenAI TTS, Browser Use) is a subscription feature — it is active whenever the user has a Nous Portal subscription, regardless of which models are used for main or aux.

API model rankings (by volume performance)

Main API — text-only (must pair with vision aux), ranked by reasoning quality:

Tier	Model	Vision	Cost	Context	Through Nous?
S	GLM 5.2	No	$0.95/$3.00 (OR) / $1.40/$4.40 (Z.AI)	1M	✅ `z-ai/glm-5.2`
S	Qwen 3.7 MAX	No	$1.25/$3.75	1M	✅ `qwen/qwen3.7-max`
S	DeepSeek V4 Pro	No	FREE (NIM) / $0.435/$0.87 (DS)	1M	✅ `deepseek/deepseek-v4-pro`
SF	DeepSeek V4 Flash	No	FREE (NIM) / $0.09/$0.18 (OR)	1M	✅ `deepseek/deepseek-v4-flash`
SF	Mimo V2.5 Pro	No	~$1.00/$3.00	1M	✅ `xiaomi/mimo-v2.5-pro`

Vision-capable models (main or aux):

Tier	Model	Cost	Context	Through Nous?
SF	MiniMax M3	FREE (NIM) / ~$0.30/$1.20	1M	✅ `minimaxai/minimax-m3`
SF	Qwen 3.7 Plus	$0.32/$1.28 (OR)	1M	✅ `qwen/qwen3.7-plus`
F	Mimo V2.5	$0.105/$0.28	1M	✅ `xiaomi/mimo-v2.5`
F	Qwen 3.6 Plus	FREE (OR preview)	1M	❌ OR only
SD	Qwen 3.5 Flash	$0.065/$0.26	1M	✅ `qwen/qwen3.5-flash-02-23`

Aux API — ranked by vision > speed > cost, free first:

Tier	Model	Vision	Cost	Context
F	Qwen 3.6 Plus	Yes	FREE (OR)	1M
SF	MiniMax M3	Yes	FREE (NIM)	1M
F	Mimo V2.5	Yes	$0.105/$0.28	1M
SD	Qwen 3.5 Flash	Yes	$0.065/$0.26	1M

See references/api-pairing-matrix.md for optimal main+aux pairings at each price point and context level.

Pitfalls

Restart=on-failure means clean stops stay dead. If the scaling watcher runs systemctl stop on a daemon (exit 0), systemd won't restart it even though the unit is enabled. Combined with the watcher itself stopping (manual, crash, systemctl), all models stay dead until manual intervention. Fix: use Restart=always on all turbofit daemon units AND the watcher itself. But note: systemctl stop still blocks Restart=always — the watcher must not use systemctl stop to pause daemons; use signal-based mechanisms or let idle timeout handle it.
turbofit-gateway now handles local-model failures gracefully (v2). When a request hits the gateway and the local model is LOADING (port bound but model not yet serving), the gateway stalls the request with backoff up to TURBOFIT_STALL_TIMEOUT (default 90s) before falling through to the API. If the local model is genuinely DEAD, the gateway auto-falls-back to the API chain in preferences.yaml (api_fallback) — it does NOT 503. If the local model returns 4xx/5xx during a request, the gateway tries the next backend in the ladder. This means a Hermes user never sees "model provider failed after retries" because of a local-model issue — they get an answer from the API fallback instead. See scripts/turbofit-gateway.py for the full implementation.
Per-GPU absolute free VRAM causes false contraction. When Darwin is loaded healthy, GPU 0 has ~1.5GB free. The old contraction logic used min_free_gb < 2.0 as the STOP_AUX trigger — meaning Darwin being loaded looked identical to external VRAM pressure. Fix: contract on non_turbofit_used (external VRAM only). Turbofit's own models consuming VRAM is expected — only contract when something ELSE hogs VRAM.
Watcher cascades all levels in one poll tick. If target_level jumps 0→4, the old code executed shrink ctx, expert offload, swap model, AND stop aux in a single iteration. Combined with the false-positive above, this killed both models within seconds of Darwin loading. Fix: cap contraction to one level per poll and add startup grace period (60s) + stability check (2 polls).
Service name mapping points to old disabled units. The watcher's short_map hardcoded "darwin-28b-reason": "darwin.service" (disabled, legacy) instead of turbofit-darwin-28b-reason.service. Fix: update short_map to point to turbofit-managed daemon services. See references/scaling-watcher-architecture.md for full details.
serve auto main double-launches on occupied ports. If a systemd daemon is already running for the picked model, serve_main still calls launch_server, which tries to bind the same port and hangs in the health-check loop. Fixed by checking systemctl --user is-active turbofit-${alias}.service and PID files before launching — if already running, skip launch and just wire Hermes config.
serve herm crashes with set -euo pipefail + shift. The case statement had shift; serve_herm "$@" but after the command parser already shifted past the command name, the extra shift fails on empty args and set -e causes silent exit. Fixed by removing the redundant shift.
start_ui doesn't recognize herm_main/herm_aux UI values. serve_herm sets UI="herm_main" or UI="herm_aux" but start_ui only matched the case herm). The unrecognized case falls through to "Unknown UI" error. Fixed by matching herm|herm_main|herm_aux).
--main-gpu must be 0 when CUDA_VISIBLE_DEVICES is set. The llama-proxy sets CUDA_VISIBLE_DEVICES=<gpu_id> which makes only one GPU visible to llama-server. But the catalog's --main-gpu N flag uses the physical GPU index. When CUDA_VISIBLE_DEVICES=1 is set, --main-gpu 1 is invalid (only device 0 exists). Fixed in llama-proxy by rewriting --main-gpu to 0 in the extra args before spawning.
LLAMA_LIB_DIR must be set in the systemd service, not just LD_LIBRARY_PATH. The llama-proxy reads LLAMA_LIB_DIR (not LD_LIBRARY_PATH) to set the library path for the spawned backend process. If only LD_LIBRARY_PATH is set, the proxy uses its default (stock llama.cpp path) and the atomic fork's symbols aren't found.
--cpu-moe-draft and --mmproj-draft are not supported by the current atomic fork. The generate_string function was adding these automatically for MoE models with spec decoding. Removed — the draft model shares the same GGUF, so --cpu-moe applies to both automatically.
Atomic fork clip loader crashes on 35B-A3B mmproj. The atomic fork's clip_model_loader::load_tensors aborts on ggml_backend_buffer_set_usage when loading the 35B-A3B mmproj (n_embd=2048). Stock llama.cpp loads it fine. Fix: remove the binary: field from the catalog entry so it uses stock llama-server, and switch presets from turbo2-kv to q8-kv (stock doesn't support turbo cache types). Also remove nextn/draft-mtp presets if GPU VRAM is tight — the draft model loads a second copy of the GGUF, doubling VRAM. Stock supports draft-mtp but not nextn.
Systemd services override turbofit. If legacy llama-*.service or omni-va.service units are running, they will hold ports and restart killed processes, silently replacing turbofit-managed launches. Before serve <alias>, run systemctl --user list-units | grep llama and stop+disable any conflicting units on the target port.
YAML duplicate keys silently overwrite. When editing models.yaml, never paste a new entry that shares keys with the one above it (e.g. two role: lines, two mmproj: lines). YAML last-key-wins means the earlier value is silently discarded. Always verify with grep after editing.
mmproj must match the text model's n_embd. A 27B-dense mmproj (n_embd=5120) will NOT work with a 35A3B MoE (n_embd=2048). The error is mismatch between text model (n_embd = X) and mmproj (n_embd = Y). For Qwen3.6-35B-A3B MoE models, use the mmproj from unsloth/Qwen3.6-35B-A3B-GGUF (mmproj-BF16.gguf, ~889MB). For 27B dense models, use the per-model F32 mmproj.
Symlink mmproj files can be stale. Several model directories had mmproj-F32.gguf symlinked to a different model's mmproj (e.g. Carnice → Qwopus 27B). Always verify with ls -la and readlink -f. If the target is wrong, delete the symlink and download the correct file from the matching HF repo.
serve <alias> may report success but launch the wrong model if a stale systemd service or leftover process holds the port. Always verify with ps aux | grep <port> that the actual command line matches the catalog entry.
The serve script reads the catalog fresh each time. If you edit models.yaml while a serve command is running, the next invocation will pick up the changes. But the currently running process won't be affected — you must serve stop <alias> and re-launch.
GitHub repo creation requires explicit user permission. Never create new repos or publish anything publicly without the user explicitly asking. Existing repos can be updated when the user directs work on them.

mmproj Reference

Vision-capable models need the correct mmproj file. Architecture mismatch (n_embd difference) causes a hard crash at load time.

Model family	n_embd	mmproj source	File
Qwen3.6-27B dense (Darwin Reason, Prism Eagle, Carwin, Qwopus, Qwable)	5120	Per-model `mmproj-F32.gguf` in GGUF dir	mmproj-F32.gguf
Qwen3.6-35B-A3B MoE (Carnice, Darwin APEX)	2048	`unsloth/Qwen3.6-35B-A3B-GGUF`	mmproj-BF16.gguf
Qwen2.5-Omni-3B	2048	Per-model dir	mmproj-F32.gguf

Check before launch: ls -la <model_dir>/mmproj*.gguf — if it's a symlink, verify the target is the right architecture.

7. HF download: use exact filenames, never wildcards

hf download <repo> --include="*.gguf" downloads every quant in the repo (BF16, F16, Q8_0, Q2_K, Q3_K_S, ...). For Qwen/Coder/Darwin repos this can be 100-200 GB of junk. Only Q4_K_M + mmproj are needed.

Correct pattern:

hf download <repo> --include="*Q4_K_M*" --include="mmproj-F32.gguf" --local-dir <dest>

Even safer: download only the one specific file you need:

hf download <repo> --include="<exact-filename>.gguf" --local-dir <dest>

8. `serve` in PATH is Ray's CLI (v5)

Ray ships a serve Python script at ~/.local/bin/serve. The turbofit bash shim (in turbofit.sharco) overrides it as a function. If you bypass the shim, you'll hit Ray. Use tf as a future alias if you want a separate namespace.

2. Per-model `binary:` is REQUIRED for TurboQuant + NextN models

Stock llama-server doesn't support -ctk turbo4 or --spec-type nextn. If you remove the binary: field from darwin-28b-reason or qwopus-27b-coder-mtp, the launch will fail with "Unsupported cache type" or "unknown speculative type".

The atomic fork lives at ~/projects/LLM-Infra/llama.cpp-atomic/build/bin/llama-server.

3. Context floor is enforced everywhere

Every launch string uses ctx >= 65536. Hermes-Agent crashes on first multi-turn message if ctx < 64K.

4. `flash_attn` is tri-state

-fa on / -fa off / -fa auto. Bare --flash-attn errors out.

5. `--gpu` is not a valid llama.cpp flag

Use -ngl N to control GPU layers, or --device CUDA0 + --main-gpu 0 for device selection. The catalog's gpu: field maps to --main-gpu N automatically.

6. NVIDIA NIM has TWO tiers — free and paid serverless

The 5 models listed all have free endpoints at https://integrate.api.nvidia.com/v1 — covered by your NVIDIA_API_KEY from a free build.nvidia.com signup (~1000 RPM, no credit card).

7. Never create new GitHub repos without explicit user permission

Existing repos can be updated when the user directs work on them. Publishing, creating, or making anything public always requires the user to say so first.

NVIDIA also sells a paid serverless tier with the same model IDs (e.g. $1.30/$2.60 for DeepSeek V4 Pro) under the same base URL. The same API key works for both, but the free tier has tighter rate limits.

7. The Omni/Senter training artifacts and OmniStep are excluded

OmniStep-SFT-8B was removed from the fleet catalog in v1.1.0 — it's no longer a fleet pick. The 12 other Omni*/Senter* training artifacts are user-made Darwin-merged models for training research. They live in the legacy catalog but not in the turbofit catalog. Add them back with name <alias> <path> if needed.

8. Hermes Desktop → Android APK via Capacitor

The Hermes Desktop app (apps/desktop/) is an Electron + React + Vite + Tailwind app. It can be wrapped for Android using Capacitor:

# 1. Install deps + Capacitor (from the Hermes Agent repo)
npm install --workspace apps/desktop
npm install --workspace apps/desktop @capacitor/core @capacitor/cli @capacitor/android

# 2. Init Capacitor (in apps/desktop/)
cd apps/desktop && npx cap init "Hermes" "com.nousresearch.hermes" --web-dir dist

# 3. Add Android platform
npx cap add android

# 4. Build the Vite renderer
npm run build

# 5. Sync to Android
npx cap sync android

# 6. Build APK (requires Android SDK)
export ANDROID_HOME=~/android-sdk
cd android && ./gradlew assembleDebug

# 7. Install on phone
adb install app/build/outputs/apk/debug/app-debug.apk

Key insight: The Desktop app's React renderer talks to the Hermes gateway via WebSocket — it doesn't need Electron's Node.js backend. On Android, it connects to the gateway running on the host machine. The gateway URL is configured at first launch.

Android SDK setup (if not installed):

mkdir -p ~/android-sdk/cmdline-tools
cd /tmp && curl -sSL -o cmdline-tools.zip \
  "https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip"
unzip -q cmdline-tools.zip -d ~/android-sdk/cmdline-tools/
mv ~/android-sdk/cmdline-tools/cmdline-tools ~/android-sdk/cmdline-tools/latest
export ANDROID_HOME=~/android-sdk
yes | ~/android-sdk/cmdline-tools/latest/bin/sdkmanager --licenses
sdkmanager "platforms;android-34" "build-tools;34.0.0" "platform-tools"

Extending turbofit

Removing a model from the catalog

# Use Python to safely remove an entry from models.yaml
python3 -c "
import yaml
with open('$HOME/.config/turbofit/models.yaml') as f:
    cfg = yaml.safe_load(f)
if '<alias>' in cfg.get('models', {}):
    del cfg['models']['<alias>']
    print('REMOVED <alias>')
with open('$HOME/.config/turbofit/models.yaml', 'w') as f:
    yaml.dump(cfg, f, default_flow_style=False, sort_keys=False)
print(f'Total models: {len(cfg[\"models\"])}')
"

After removal, also check if the model appears in the scaling ladder (references/scaling-ladder.md or references/curated-lineup.md) and remove/update those references.

Adding a new model

name mynew /path/to/file.gguf --port 11530
# then edit ~/.config/turbofit/models.yaml to add tier, presets, gpu, etc.
serve mynew

Adding a new flag preset

Edit ~/.hermes/skills/turbofit/scripts/serve, find declare -A PRESET_FLAGS=(, add a line like:

[my-preset]='--my-flag value'

Adding a new tier

Edit scale_pick() and list_catalog() in the same script.

Adding a new launcher

Extend the four declare -A LAUNCHER_* arrays at the top, add cases in install_launcher(), update_all_launchers(), generate_string(), and launch_server().

Mixture of Agents (MoA) Integration

What is MoA?

Hermes MoA (Mixture of Agents) runs reference models (analysis layer, no tool schemas) before the aggregator (final response + tool schemas). This produces higher-quality responses than any single model alone. Reference models provide diverse perspectives; the aggregator synthesizes them into the final answer.

Reference models: Run first, no tool schemas. Can fail without aborting the turn.
Aggregator: Runs second, with tool schemas. Produces the final response. Cannot be another MoA preset (no recursion).
Config location: Hermes configuration file under the moa: key.

Docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/mixture-of-agents

Available presets

Preset	References	Aggregator	Use Case
`default`	Darwin + DeepSeek V4 Pro	GLM 5.2	Best quality, balanced cost
`local`	Carnice	Darwin	Zero API cost, both local
`reasoning`	Darwin + DeepSeek V4 Pro + Qwen 3.7 MAX	GLM 5.2	Maximum reasoning power
`fast`	Carnice	DeepSeek V4 Flash	Speed-optimized
`review`	Darwin + DeepSeek V4 Pro	GLM 5.2	Code review (low temp)

`serve moa` commands

serve moa list                    # List configured MoA presets
serve moa use <preset>            # Print command to switch to a preset
serve moa shot <prompt>           # Print command for one-shot MoA query
serve moa status                  # Show active preset details
serve moa presets                 # Show full preset details (models, temps, max_tokens)
serve moa recommend               # Hardware-aware preset recommendation

The serve script can't invoke Hermes slash commands directly, so use and shot print the command for the user to run:

Activate: /model <preset> --provider moa
One-shot: /moa <prompt>

`serve moa recommend` logic

Probes current hardware state and recommends the best preset:

Runs probe_vram (nvidia-smi) for free VRAM
Curls :11500 (Darwin) and :8082 (Carnice) to check which local models are alive
Recommends:
- Both local running + VRAM healthy (≥8GB free) → local (zero cost)
- Both local running + VRAM pressured (<8GB) → fast (API aggregator)
- Only Darwin running → default (Darwin ref + API ref + API aggregator)
- Only Carnice running → fast (Carnice ref + API aggregator)
- Nothing local running → reasoning (all API, max diversity)

Creating custom presets

Edit your Hermes configuration and add a new entry under moa.presets:

moa:
  presets:
    my-custom:
      reference_models:
        - provider: custom:llama-main
          model: Darwin-28B-REASON.Q4_K_M.gguf
        - provider: nous
          model: deepseek/deepseek-v4-pro
      aggregator:
        provider: nous
        model: z-ai/glm-5.2
      reference_temperature: 0.6
      aggregator_temperature: 0.4
      max_tokens: 8192
      enabled: true

Providers: custom:llama-main, custom:llama-aux, nous (Nous API). The aggregator cannot be another MoA preset.

VRAM considerations

MoA with local models = 2x+ compute per iteration (reference + aggregator both use GPU)
When VRAM pressure hits and a local model is the MoA aggregator, swap to an API aggregator
The local preset uses both GPUs (Carnice ref on GPU 1, Darwin aggregator on GPU 0) — monitor VRAM
The fast preset offloads the aggregator to API, keeping only one local model active
Use serve moa recommend for hardware-aware suggestions before activating a preset

Design rules (hardware-neutral, learned this session)

No multi-GPU hardcoding. Tensor split is user-configurable via catalog extra_args: ['--tensor-split', 'X,Y']. No preset assumes dual-GPU or specific VRAM amounts.
tok/s varies by GPU. Catalog tok_s_target values are measured locally — mark them as approximate. Use relative ranking (tier priority > vision bonus > speed bonus) rather than absolute claims.
All models get vision. Every local Qwen-family model can use the same mmproj-F32.gguf via symlink. Set vision: true and mmproj: in every catalog entry. One mmproj file serves the entire fleet.
Model picks ARE the cascade. The slot-ordered picks are the hand-chosen scale-down sequence — not generic examples. When building recommendations, preserve the actual model aliases and their tier assignments.
No workarounds — fix the root cause. When a backend fails to load, fix the binary, flags, or catalog entry. Do NOT disable features as a workaround. Do NOT route around turbofit to another backend. The whole point of turbofit is that ALL local model servers are managed through it.
If a model can't load with one binary, try the other. The atomic fork has turbo KV cache and NextN but crashes on 35B-A3B mmproj. Stock has q8 KV and draft-mtp but no turbo/nextn. Use whichever binary makes the model work. See references/binary-selection.md for the decision tree.

name	turbofit
description	Opinionated unified LLM backend (turbofit v1.1). Picks the best main + aux model for your hardware — local or API — launches them detached, wires Hermes-Agent config, and adapts to live VRAM pressure via a scaling ladder. Three hardware tiers: Beefy (local+local), Modest (API+local), Thin (API+API). `serve auto main` auto-detects GPU and suggests the right setup. API fallback is always available (free: DeepSeek V4 Pro + Kimi K2.6). Replaces llama-launch, omni-va, and ad-hoc llama-server scripts. Catalog schema supports per-model binary pinning (atomic fork vs stock), named flag presets (nextn, draft-mtp, turbo4-kv, vision-mmproj), tier ladder (s/sf/sd/f/c), and the 64K Hermes context floor is enforced everywhere. End-user UX is `serve auto main` and the user is done.
version	1.1.0
author	SouthpawIN + Nous Girl
license	MIT
tags	["llama.cpp","ollama","vllm","sglang","llmfit","gguf","hermes-agent","nvidia-nim","catalog","scaling-ladder","opinionated","unified-backend"]
metadata	{"hermes":{"tags":["llama.cpp","ollama","vllm","sglang","llmfit","gguf","hermes-agent","nvidia-nim","catalog","scaling-ladder","opinionated","unified-backend"],"related_skills":["southpaw-models","local-llm-fleet-management","llama-cpp","gguf-quantization","omni-va-local-server","evolutionary-radio"]},"changelog":"5.0.0 (2026-06-22): Opinionated unified backend.\n - 18 named flag presets: nextn/nextn-tight, draft-mtp/draft-mtp-tight, turbo4/3/2-kv, q8/q4-kv, cpu-moe-2/4/8, no-mmap, split-none, mlock, parallel-4/2/1.\n - Per-model binary pin: atomic fork for TurboQuant+NextN models, stock for legacy.\n - New commands: serve vram, serve auto [main\|aux], serve downscale, serve stop-all, serve fetch, serve bench, serve recommend.\n - serve recommend ranks catalog by ctx≥64K, tok/s≥25, vision bonus, tier priority — all hardware-neutral (tok/s measured locally, varies by GPU).\n - All local models vision-enabled via shared mmproj-F32.gguf symlink.\n - serve herm fixed: launches herm TUI only (herm handles hermes internally — no double-launch).\n - NIM API ranking: DeepSeek V4 Pro → GLM 5.1 → DS V4 Flash → MiniMax M3 → Nemotron Ultra.\n - Default aux: Step 3.7 Flash (free, vision). Fallback: MiniMax M3 (free, vision, 1M ctx) → Qwen 3.5 Flash 02-23 (paid, vision, 1M ctx).\n - Multi-GPU tensor split via catalog extra_args (no hardcoded GPU values — hardware-neutral).\n - Migrated 23 curated picks from llama-launch catalog; 13 dead launchers consolidated.\n - Replaces: llama-launch, omni-va, model-server.sh, start-{qwen,glm,...}-server.sh.\n4.0.0: Initial multi-launcher (llama.cpp / Ollama / vLLM / SGlang), NVIDIA NIM API list.\n"}

Turbofit v5 — opinionated unified backend

End-user UX is serve auto main — picks the best main model for your system state, launches it detached, and wires Hermes config. The user is done.

For the scaling/adaptation half of the opinionated story, see references/scaling-ladder.md.

When to use

Quick start

# Source the shim (one-time setup)
source <hermes-skills-dir>/turbofit/scripts/turbofit.sharco

# One-shot: pick best main for your box, launch, wire Hermes
serve auto main                    # opinionated: auto-detects hardware, picks local or API
serve auto main --vision           # require vision
serve auto main --api              # force API mode (no local GPU needed)
serve auto main --free             # only free API endpoints
AUTO_CTX=131072 serve auto main    # raise ctx target

# Stop everything
serve stop-all

# Browse the catalog (featured first, tier-ordered)
serve catalog

# Launch a specific model
serve darwin-28b-reason
serve string darwin-apex-36b-i-compact   # print launch string, don't launch

# Register a new model
name mymodel /path/to/file.gguf --port 9090

# Wire to Hermes (main / aux)
serve main mymodel --ui tui
serve aux mymodel --ui tui

# VRAM probe (live)
serve vram

# Adapt to current VRAM pressure
serve downscale

Dynamic Model Database

The model universe, pricing, and benchmarks are stored in references/model-database.yaml — a single source of truth that is auto-updated daily by a research cron job.

How it works

Daily research cron runs at 6am — scans OpenRouter, HuggingFace, Price Per Token, and LLMCheck for new models, pricing changes, and benchmark updates
Research script (scripts/research-models.py) fetches live data and generates a report
Agent reviews the report, updates model-database.yaml with new models or pricing changes
GitHub sync (scripts/sync-github.sh) pushes updates to SouthpawIN/turbofit (primary) and SouthpawIN/sovth-config (collection)
All turbofit users get fresh data by running hermes skills update turbofit

What's in the database

Each model entry includes:

Pricing across all providers (Nous, OpenRouter, NIM, direct API)
Context window and supported context tiers
Vision capability (text-only models must pair with vision aux)
Benchmark scores (MMLU, SWE-Verified, HumanEval, AIME — when available)
Local model info (GGUF repo, quants, size, archetypes, mmproj)
Discovery date and last verified date

Manual usage

# Run the research script manually
python3 ~/.hermes/skills/turbofit/scripts/research-models.py

# Check the latest report
cat ~/.hermes/skills/turbofit/references/research-report.md

# Sync to GitHub manually (pushes to SouthpawIN/turbofit + SouthpawIN/sovth-config)
bash ~/.hermes/skills/turbofit/scripts/sync-github.sh

# Update turbofit skill (for end users)
hermes skills update turbofit

Opinionated defaults (turbofit v5)

Setting	Value	Source
ctx floor	65536 tokens	Hermes-Agent hard requirement
tok/s floor (main)	25 tok/s	Spec decoding assumption
prefer vision on main	yes	Use `vision:` field in catalog
tier ladder	`s → sf → sd → f → c`	`serve auto`
scale down triggers	free VRAM < 14GB → < 8GB → < 4GB	`serve downscale`
per-model binary pin	atomic (TurboQuant+NextN) or stock	`binary:` field

End-user UX: serve auto main → done. If VRAM pressure hits, serve downscale adapts.

Catalog schema (v5 extended)

models:
  <alias>:
    # Required
    launcher: llama-cpp           # llama-cpp | ollama | vllm | sglang
    path: /abs/path/to.gguf        # OR HF repo for vllm/sglang
    port: 11500                    # auto-assigned if absent

    # Recommended
    ctx: 262144                    # 64K floor enforced
    gpu: 0                         # 0 | 1 (single-GPU target)
    gpu: 0                         # 0 | 1 (single-GPU target)
    mmproj: /path/to/mmproj.gguf   # vision projector
    presets: [nextn, turbo4-kv, no-mmap]   # see below
    extra_args: [--draft-block-size 3]           # raw flag list
    aliases: [short, alt]          # alternative names resolved by serve的政策
    description: "..."             # shown in `serve catalog`
    tags: [qwen, mtp, featured]

    # Opinionated metadata (used by `serve auto` and Garage UI)
    tier: s                        # s | sf | sd | f | c
    featured: true                 # Garage top row
    tok_s_target: 107              # measured throughput
    vision: true                   # has vision tower + mmproj
    size_gb: 16.0                  # disk footprint
    hf_repo: org/repo              # for `serve fetch <alias>` (future)
    role: main                     # main | aux | either

Named flag presets

Apply by listing in presets:. Multiple presets merge; later presets override earlier flags.

Preset	Expands to
`nextn`	`--spec-type nextn --draft-block-size 3`
`nextn-tight`	`--spec-type nextn --draft-block-size 2`
`draft-mtp`	`--spec-type draft-mtp`
`draft-mtp-tight`	`--spec-type draft-mtp --draft-block-size 2`
`turbo4-kv`	`-ctk turbo4 -ctv turbo4`
`turbo3-kv`	`-ctk turbo3 -ctv turbo3`
`turbo2-kv`	`-ctk turbo2 -ctv turbo2`
`q8-kv`	`-ctk q8_0 -ctv q8_0`
`q4-kv`	`-ctk q4_0 -ctv q4_0`
`no-mmap`	`--no-mmap`
`split-none`	`--split-mode none`
`mlock`	`--mlock`
`cpu-moe-2` / `cpu-moe-4` / `cpu-moe-8`	`--n-cpu-moe N` (MoE expert offload)
`parallel-4` / `parallel-2` / `parallel-1`	`--parallel N`

Multi-GPU tensor split: use extra_args: ['--tensor-split', 'X,Y'] in catalog — no hardcoded preset.

Tier ladder (used by `serve auto`)

Tier	Meaning	Examples
`s`	smartest	Darwin Reason, Darwin Apex-Compact, Prism Eagle
`sf`	smart + fast	Carwin-MTP, Qwopus v2-MTP, Qwopus Coder-MTP
`sd`	smart + dense	Carnice Apex Compact
`f`	fast	Qwable MTP, Qwopus abliterated-MTP
`c`	cheap	Qwen legacy, devstral, step-flash, omni-3b

Commands

# Install / update
serve install                            # llama.cpp from source (atomic fork if path set)
serve install <launcher>                 # one launcher: llama-cpp, ollama, vllm, sglang
serve update                             # update llama.cpp
serve update <launcher|all>              # specific launcher or all
serve check                              # version status

# Hardware / VRAM
serve fit <model> [ctx]                  # llmfit fit check (default ctx=65536)
serve vram                               # live GPU VRAM probe (JSON)
serve recommend                          # scan catalog, rank by fit (ctx≥64K, tok/s≥25, Q4, vision)

# Catalog
serve register <alias> <path>            # register model
           [--launcher llama-cpp|ollama|vllm|sglang] [--port N]
serve catalog                            # show registered (featured first, tier-ordered)

# Launch
serve <alias>                            # launch detached, shows backend/port/logs
serve string <alias>                     # print launch string, don't launch
serve stop <alias>                       # stop a running server
serve stop-all                           # stop everything
serve list                               # list running + detect rogue llama-servers

# Fetch / benchmark
serve fetch <alias>                      # download missing model from HF (uses hf_repo)
serve bench <alias>                      # lm-eval-harness benchmark (launches if needed)

# Opinionated auto (turbofit v1.1 — hardware-aware, API-aware)
serve auto main [--vision] [--api] [--free] [--ui ...]    # pick best main (auto-detects hardware, picks local or API)
serve auto aux [--vision] [--api] [--free] [--ui ...]     # pick best aux
serve downscale                          # adapt to current VRAM pressure
AUTO_CTX=131072 serve auto main          # override ctx target

# Hardware-aware auto-detection:
#   ≥24GB VRAM → local main + local aux (Beefy)
#   8-24GB VRAM → API main + free/cheap aux (Modest)
#   <8GB / no GPU → API main + API aux, zero cost with free endpoints (Thin)
# Use --api to force API mode, --free to restrict to free endpoints
#
# API pairings use the model universe from references/api-pairing-matrix.md
# Gateway indicators: 🟢 NOUS+TG (full Tool Gateway), 🟡 NOUS+OR, 🟠 NOUS+NIM, ⚪ NIM (free, no TG)

# Hermes routing
serve main <alias> [--ui tui|dashboard|gateway|desktop|herm]
serve aux  <alias> [--ui ...]
serve herm <alias>                       # launch + main + herm TUI (herm handles hermes internally)
serve herm aux <alias>
serve herm                               # auto-pick main + launch herm TUI

# NVIDIA NIM API (curated)
serve api list
serve api use <rank|api_id> [main|aux]

How the auto-picker decides

Filter catalog by role (main | aux) and (if --vision) vision: true.
Filter by ctx >= target (default 65536).
Sort by (tier_rank, featured, -tok_s_target) — best tier wins, then featured, then speed.
Skip entries whose path: doesn't exist (catalog has them but disk doesn't).
Launch via the per-model binary: if set, else stock llama-server.

The auto-picker does NOT yet weight by current VRAM headroom. Use serve downscale to adapt after the fact, or run serve vram first to know.

How the scaling ladder works (v1.1 — 3 hardware tiers, universal)

The ladder covers three hardware profiles and is hardware-neutral — any user can plug in their GPU and get the right setup. See references/scaling-ladder.md for full step-by-step details.

Hardware Tiers (auto-detected by `serve auto`)

Tier	VRAM	Typical GPUs	Default Main	Default Aux
Beefy	≥24GB	Dual GPU or single 24GB+	Local 27-28B dense (Q4)	Local 35B MoE 3B-active
Modest	8-24GB	RTX 3060/4060/4070 (8-16GB)	DeepSeek V4 Pro (API)	Qwen 3.6 Plus (OR free) or MiniMax M3 (NIM free)
Thin	<8GB or no GPU	Integrated graphics, no GPU	DeepSeek V4 Flash (free NIM)	MiniMax M3 (free NIM)

Context-Level Ladder (4 tiers)

Level	Context	Local VRAM (Beefy)	API Price Tier
1	1M	~40GB+ (dual GPU)	Free-Budget
2	512K	~28GB (pressured)	Budget-Mid
3	262K	~16GB (single GPU)	Mid-Premium
4	132K	~8GB (survival)	Budget

Beefy-tier scaling ladder (7 steps, triggered by `serve downscale`)

Uses model archetypes — users register their own local models that match the archetype. serve recommend scans the catalog and picks the best fit.

Step  1: Ideal          — 27-28B dense (Q4) + 35B MoE (3B active) aux, both @ 1M
Step  2: Mild pressure  — Offload aux MoE experts to CPU (--cpu-moe), ~10 tok/s
Step  3: Moderate       — Drop both models' context to 512K
Step  4: High pressure  — Drop local aux, route aux to API (free vision model)
Step  5: Swap main       — Swap to 27B hybrid/Mamba (lighter, ~14 GB)
Step  6: MoE main        — Swap to 35B MoE 3B-active main + API aux @ 132K
Step  7: API-only       — No local serving viable. API main + API aux. Zero cost with free endpoints.

API Pairing Matrix

Gateway indicators:

🟢 NOUS — Both through Nous
🟡 NOUS+OR — Main through Nous, aux through OpenRouter (10% bonus)
🟠 NOUS+NIM — Main through Nous, aux through NIM (free)
⚪ NIM — Both through NIM (free)

API model rankings (by volume performance)

Main API — text-only (must pair with vision aux), ranked by reasoning quality:

Tier	Model	Vision	Cost	Context	Through Nous?
S	GLM 5.2	No	$0.95/$3.00 (OR) / $1.40/$4.40 (Z.AI)	1M	✅ `z-ai/glm-5.2`
S	Qwen 3.7 MAX	No	$1.25/$3.75	1M	✅ `qwen/qwen3.7-max`
S	DeepSeek V4 Pro	No	FREE (NIM) / $0.435/$0.87 (DS)	1M	✅ `deepseek/deepseek-v4-pro`
SF	DeepSeek V4 Flash	No	FREE (NIM) / $0.09/$0.18 (OR)	1M	✅ `deepseek/deepseek-v4-flash`
SF	Mimo V2.5 Pro	No	~$1.00/$3.00	1M	✅ `xiaomi/mimo-v2.5-pro`

Vision-capable models (main or aux):

Tier	Model	Cost	Context	Through Nous?
SF	MiniMax M3	FREE (NIM) / ~$0.30/$1.20	1M	✅ `minimaxai/minimax-m3`
SF	Qwen 3.7 Plus	$0.32/$1.28 (OR)	1M	✅ `qwen/qwen3.7-plus`
F	Mimo V2.5	$0.105/$0.28	1M	✅ `xiaomi/mimo-v2.5`
F	Qwen 3.6 Plus	FREE (OR preview)	1M	❌ OR only
SD	Qwen 3.5 Flash	$0.065/$0.26	1M	✅ `qwen/qwen3.5-flash-02-23`

Aux API — ranked by vision > speed > cost, free first:

Tier	Model	Vision	Cost	Context
F	Qwen 3.6 Plus	Yes	FREE (OR)	1M
SF	MiniMax M3	Yes	FREE (NIM)	1M
F	Mimo V2.5	Yes	$0.105/$0.28	1M
SD	Qwen 3.5 Flash	Yes	$0.065/$0.26	1M

See references/api-pairing-matrix.md for optimal main+aux pairings at each price point and context level.

Pitfalls

Restart=on-failure means clean stops stay dead. If the scaling watcher runs systemctl stop on a daemon (exit 0), systemd won't restart it even though the unit is enabled. Combined with the watcher itself stopping (manual, crash, systemctl), all models stay dead until manual intervention. Fix: use Restart=always on all turbofit daemon units AND the watcher itself. But note: systemctl stop still blocks Restart=always — the watcher must not use systemctl stop to pause daemons; use signal-based mechanisms or let idle timeout handle it.
turbofit-gateway now handles local-model failures gracefully (v2). When a request hits the gateway and the local model is LOADING (port bound but model not yet serving), the gateway stalls the request with backoff up to TURBOFIT_STALL_TIMEOUT (default 90s) before falling through to the API. If the local model is genuinely DEAD, the gateway auto-falls-back to the API chain in preferences.yaml (api_fallback) — it does NOT 503. If the local model returns 4xx/5xx during a request, the gateway tries the next backend in the ladder. This means a Hermes user never sees "model provider failed after retries" because of a local-model issue — they get an answer from the API fallback instead. See scripts/turbofit-gateway.py for the full implementation.
Per-GPU absolute free VRAM causes false contraction. When Darwin is loaded healthy, GPU 0 has ~1.5GB free. The old contraction logic used min_free_gb < 2.0 as the STOP_AUX trigger — meaning Darwin being loaded looked identical to external VRAM pressure. Fix: contract on non_turbofit_used (external VRAM only). Turbofit's own models consuming VRAM is expected — only contract when something ELSE hogs VRAM.
Watcher cascades all levels in one poll tick. If target_level jumps 0→4, the old code executed shrink ctx, expert offload, swap model, AND stop aux in a single iteration. Combined with the false-positive above, this killed both models within seconds of Darwin loading. Fix: cap contraction to one level per poll and add startup grace period (60s) + stability check (2 polls).
Service name mapping points to old disabled units. The watcher's short_map hardcoded "darwin-28b-reason": "darwin.service" (disabled, legacy) instead of turbofit-darwin-28b-reason.service. Fix: update short_map to point to turbofit-managed daemon services. See references/scaling-watcher-architecture.md for full details.
serve auto main double-launches on occupied ports. If a systemd daemon is already running for the picked model, serve_main still calls launch_server, which tries to bind the same port and hangs in the health-check loop. Fixed by checking systemctl --user is-active turbofit-${alias}.service and PID files before launching — if already running, skip launch and just wire Hermes config.
serve herm crashes with set -euo pipefail + shift. The case statement had shift; serve_herm "$@" but after the command parser already shifted past the command name, the extra shift fails on empty args and set -e causes silent exit. Fixed by removing the redundant shift.
start_ui doesn't recognize herm_main/herm_aux UI values. serve_herm sets UI="herm_main" or UI="herm_aux" but start_ui only matched the case herm). The unrecognized case falls through to "Unknown UI" error. Fixed by matching herm|herm_main|herm_aux).
--main-gpu must be 0 when CUDA_VISIBLE_DEVICES is set. The llama-proxy sets CUDA_VISIBLE_DEVICES=<gpu_id> which makes only one GPU visible to llama-server. But the catalog's --main-gpu N flag uses the physical GPU index. When CUDA_VISIBLE_DEVICES=1 is set, --main-gpu 1 is invalid (only device 0 exists). Fixed in llama-proxy by rewriting --main-gpu to 0 in the extra args before spawning.
LLAMA_LIB_DIR must be set in the systemd service, not just LD_LIBRARY_PATH. The llama-proxy reads LLAMA_LIB_DIR (not LD_LIBRARY_PATH) to set the library path for the spawned backend process. If only LD_LIBRARY_PATH is set, the proxy uses its default (stock llama.cpp path) and the atomic fork's symbols aren't found.
--cpu-moe-draft and --mmproj-draft are not supported by the current atomic fork. The generate_string function was adding these automatically for MoE models with spec decoding. Removed — the draft model shares the same GGUF, so --cpu-moe applies to both automatically.
Atomic fork clip loader crashes on 35B-A3B mmproj. The atomic fork's clip_model_loader::load_tensors aborts on ggml_backend_buffer_set_usage when loading the 35B-A3B mmproj (n_embd=2048). Stock llama.cpp loads it fine. Fix: remove the binary: field from the catalog entry so it uses stock llama-server, and switch presets from turbo2-kv to q8-kv (stock doesn't support turbo cache types). Also remove nextn/draft-mtp presets if GPU VRAM is tight — the draft model loads a second copy of the GGUF, doubling VRAM. Stock supports draft-mtp but not nextn.
Systemd services override turbofit. If legacy llama-*.service or omni-va.service units are running, they will hold ports and restart killed processes, silently replacing turbofit-managed launches. Before serve <alias>, run systemctl --user list-units | grep llama and stop+disable any conflicting units on the target port.
YAML duplicate keys silently overwrite. When editing models.yaml, never paste a new entry that shares keys with the one above it (e.g. two role: lines, two mmproj: lines). YAML last-key-wins means the earlier value is silently discarded. Always verify with grep after editing.
mmproj must match the text model's n_embd. A 27B-dense mmproj (n_embd=5120) will NOT work with a 35A3B MoE (n_embd=2048). The error is mismatch between text model (n_embd = X) and mmproj (n_embd = Y). For Qwen3.6-35B-A3B MoE models, use the mmproj from unsloth/Qwen3.6-35B-A3B-GGUF (mmproj-BF16.gguf, ~889MB). For 27B dense models, use the per-model F32 mmproj.
Symlink mmproj files can be stale. Several model directories had mmproj-F32.gguf symlinked to a different model's mmproj (e.g. Carnice → Qwopus 27B). Always verify with ls -la and readlink -f. If the target is wrong, delete the symlink and download the correct file from the matching HF repo.
serve <alias> may report success but launch the wrong model if a stale systemd service or leftover process holds the port. Always verify with ps aux | grep <port> that the actual command line matches the catalog entry.
The serve script reads the catalog fresh each time. If you edit models.yaml while a serve command is running, the next invocation will pick up the changes. But the currently running process won't be affected — you must serve stop <alias> and re-launch.
GitHub repo creation requires explicit user permission. Never create new repos or publish anything publicly without the user explicitly asking. Existing repos can be updated when the user directs work on them.

mmproj Reference

Vision-capable models need the correct mmproj file. Architecture mismatch (n_embd difference) causes a hard crash at load time.

Model family	n_embd	mmproj source	File
Qwen3.6-27B dense (Darwin Reason, Prism Eagle, Carwin, Qwopus, Qwable)	5120	Per-model `mmproj-F32.gguf` in GGUF dir	mmproj-F32.gguf
Qwen3.6-35B-A3B MoE (Carnice, Darwin APEX)	2048	`unsloth/Qwen3.6-35B-A3B-GGUF`	mmproj-BF16.gguf
Qwen2.5-Omni-3B	2048	Per-model dir	mmproj-F32.gguf

Check before launch: ls -la <model_dir>/mmproj*.gguf — if it's a symlink, verify the target is the right architecture.

7. HF download: use exact filenames, never wildcards

Correct pattern:

hf download <repo> --include="*Q4_K_M*" --include="mmproj-F32.gguf" --local-dir <dest>

Even safer: download only the one specific file you need:

hf download <repo> --include="<exact-filename>.gguf" --local-dir <dest>

8. `serve` in PATH is Ray's CLI (v5)

2. Per-model `binary:` is REQUIRED for TurboQuant + NextN models

The atomic fork lives at ~/projects/LLM-Infra/llama.cpp-atomic/build/bin/llama-server.

3. Context floor is enforced everywhere

Every launch string uses ctx >= 65536. Hermes-Agent crashes on first multi-turn message if ctx < 64K.

4. `flash_attn` is tri-state

-fa on / -fa off / -fa auto. Bare --flash-attn errors out.

5. `--gpu` is not a valid llama.cpp flag

Use -ngl N to control GPU layers, or --device CUDA0 + --main-gpu 0 for device selection. The catalog's gpu: field maps to --main-gpu N automatically.

6. NVIDIA NIM has TWO tiers — free and paid serverless

The 5 models listed all have free endpoints at https://integrate.api.nvidia.com/v1 — covered by your NVIDIA_API_KEY from a free build.nvidia.com signup (~1000 RPM, no credit card).

7. Never create new GitHub repos without explicit user permission

Existing repos can be updated when the user directs work on them. Publishing, creating, or making anything public always requires the user to say so first.

7. The Omni/Senter training artifacts and OmniStep are excluded

8. Hermes Desktop → Android APK via Capacitor

The Hermes Desktop app (apps/desktop/) is an Electron + React + Vite + Tailwind app. It can be wrapped for Android using Capacitor:

# 1. Install deps + Capacitor (from the Hermes Agent repo)
npm install --workspace apps/desktop
npm install --workspace apps/desktop @capacitor/core @capacitor/cli @capacitor/android

# 2. Init Capacitor (in apps/desktop/)
cd apps/desktop && npx cap init "Hermes" "com.nousresearch.hermes" --web-dir dist

# 3. Add Android platform
npx cap add android

# 4. Build the Vite renderer
npm run build

# 5. Sync to Android
npx cap sync android

# 6. Build APK (requires Android SDK)
export ANDROID_HOME=~/android-sdk
cd android && ./gradlew assembleDebug

# 7. Install on phone
adb install app/build/outputs/apk/debug/app-debug.apk

Android SDK setup (if not installed):

mkdir -p ~/android-sdk/cmdline-tools
cd /tmp && curl -sSL -o cmdline-tools.zip \
  "https://dl.google.com/android/repository/commandlinetools-linux-11076708_latest.zip"
unzip -q cmdline-tools.zip -d ~/android-sdk/cmdline-tools/
mv ~/android-sdk/cmdline-tools/cmdline-tools ~/android-sdk/cmdline-tools/latest
export ANDROID_HOME=~/android-sdk
yes | ~/android-sdk/cmdline-tools/latest/bin/sdkmanager --licenses
sdkmanager "platforms;android-34" "build-tools;34.0.0" "platform-tools"

Extending turbofit

Removing a model from the catalog

# Use Python to safely remove an entry from models.yaml
python3 -c "
import yaml
with open('$HOME/.config/turbofit/models.yaml') as f:
    cfg = yaml.safe_load(f)
if '<alias>' in cfg.get('models', {}):
    del cfg['models']['<alias>']
    print('REMOVED <alias>')
with open('$HOME/.config/turbofit/models.yaml', 'w') as f:
    yaml.dump(cfg, f, default_flow_style=False, sort_keys=False)
print(f'Total models: {len(cfg[\"models\"])}')
"

After removal, also check if the model appears in the scaling ladder (references/scaling-ladder.md or references/curated-lineup.md) and remove/update those references.

Adding a new model

name mynew /path/to/file.gguf --port 11530
# then edit ~/.config/turbofit/models.yaml to add tier, presets, gpu, etc.
serve mynew

Adding a new flag preset

Edit ~/.hermes/skills/turbofit/scripts/serve, find declare -A PRESET_FLAGS=(, add a line like:

[my-preset]='--my-flag value'

Adding a new tier

Edit scale_pick() and list_catalog() in the same script.

Adding a new launcher

Extend the four declare -A LAUNCHER_* arrays at the top, add cases in install_launcher(), update_all_launchers(), generate_string(), and launch_server().

Mixture of Agents (MoA) Integration

What is MoA?

Reference models: Run first, no tool schemas. Can fail without aborting the turn.
Aggregator: Runs second, with tool schemas. Produces the final response. Cannot be another MoA preset (no recursion).
Config location: Hermes configuration file under the moa: key.

Docs: https://hermes-agent.nousresearch.com/docs/user-guide/features/mixture-of-agents

Available presets

Preset	References	Aggregator	Use Case
`default`	Darwin + DeepSeek V4 Pro	GLM 5.2	Best quality, balanced cost
`local`	Carnice	Darwin	Zero API cost, both local
`reasoning`	Darwin + DeepSeek V4 Pro + Qwen 3.7 MAX	GLM 5.2	Maximum reasoning power
`fast`	Carnice	DeepSeek V4 Flash	Speed-optimized
`review`	Darwin + DeepSeek V4 Pro	GLM 5.2	Code review (low temp)

`serve moa` commands

serve moa list                    # List configured MoA presets
serve moa use <preset>            # Print command to switch to a preset
serve moa shot <prompt>           # Print command for one-shot MoA query
serve moa status                  # Show active preset details
serve moa presets                 # Show full preset details (models, temps, max_tokens)
serve moa recommend               # Hardware-aware preset recommendation

The serve script can't invoke Hermes slash commands directly, so use and shot print the command for the user to run:

Activate: /model <preset> --provider moa
One-shot: /moa <prompt>

`serve moa recommend` logic

Probes current hardware state and recommends the best preset:

Runs probe_vram (nvidia-smi) for free VRAM
Curls :11500 (Darwin) and :8082 (Carnice) to check which local models are alive
Recommends:
- Both local running + VRAM healthy (≥8GB free) → local (zero cost)
- Both local running + VRAM pressured (<8GB) → fast (API aggregator)
- Only Darwin running → default (Darwin ref + API ref + API aggregator)
- Only Carnice running → fast (Carnice ref + API aggregator)
- Nothing local running → reasoning (all API, max diversity)

Creating custom presets

Edit your Hermes configuration and add a new entry under moa.presets:

moa:
  presets:
    my-custom:
      reference_models:
        - provider: custom:llama-main
          model: Darwin-28B-REASON.Q4_K_M.gguf
        - provider: nous
          model: deepseek/deepseek-v4-pro
      aggregator:
        provider: nous
        model: z-ai/glm-5.2
      reference_temperature: 0.6
      aggregator_temperature: 0.4
      max_tokens: 8192
      enabled: true

Providers: custom:llama-main, custom:llama-aux, nous (Nous API). The aggregator cannot be another MoA preset.

VRAM considerations

MoA with local models = 2x+ compute per iteration (reference + aggregator both use GPU)
When VRAM pressure hits and a local model is the MoA aggregator, swap to an API aggregator
The local preset uses both GPUs (Carnice ref on GPU 1, Darwin aggregator on GPU 0) — monitor VRAM
The fast preset offloads the aggregator to API, keeping only one local model active
Use serve moa recommend for hardware-aware suggestions before activating a preset

Design rules (hardware-neutral, learned this session)

No multi-GPU hardcoding. Tensor split is user-configurable via catalog extra_args: ['--tensor-split', 'X,Y']. No preset assumes dual-GPU or specific VRAM amounts.
tok/s varies by GPU. Catalog tok_s_target values are measured locally — mark them as approximate. Use relative ranking (tier priority > vision bonus > speed bonus) rather than absolute claims.
All models get vision. Every local Qwen-family model can use the same mmproj-F32.gguf via symlink. Set vision: true and mmproj: in every catalog entry. One mmproj file serves the entire fleet.
Model picks ARE the cascade. The slot-ordered picks are the hand-chosen scale-down sequence — not generic examples. When building recommendations, preserve the actual model aliases and their tier assignments.
No workarounds — fix the root cause. When a backend fails to load, fix the binary, flags, or catalog entry. Do NOT disable features as a workaround. Do NOT route around turbofit to another backend. The whole point of turbofit is that ALL local model servers are managed through it.
If a model can't load with one binary, try the other. The atomic fork has turbo KV cache and NextN but crashes on 35B-A3B mmproj. Stock has q8 KV and draft-mtp but no turbo/nextn. Use whichever binary makes the model work. See references/binary-selection.md for the decision tree.

turbofit

Turbofit v5 — opinionated unified backend

When to use

Quick start

Dynamic Model Database

How it works

What's in the database

Manual usage

Opinionated defaults (turbofit v5)

Catalog schema (v5 extended)

Named flag presets

Tier ladder (used by serve auto)

Commands

How the auto-picker decides

How the scaling ladder works (v1.1 — 3 hardware tiers, universal)

Hardware Tiers (auto-detected by serve auto)

Context-Level Ladder (4 tiers)

Beefy-tier scaling ladder (7 steps, triggered by serve downscale)

API Pairing Matrix

API model rankings (by volume performance)

Pitfalls

mmproj Reference

7. HF download: use exact filenames, never wildcards

8. serve in PATH is Ray's CLI (v5)

2. Per-model binary: is REQUIRED for TurboQuant + NextN models

3. Context floor is enforced everywhere

4. flash_attn is tri-state

5. --gpu is not a valid llama.cpp flag

6. NVIDIA NIM has TWO tiers — free and paid serverless

7. Never create new GitHub repos without explicit user permission

7. The Omni*/Senter* training artifacts and OmniStep are excluded

8. Hermes Desktop → Android APK via Capacitor

Extending turbofit

Removing a model from the catalog

Adding a new model

Adding a new flag preset

Adding a new tier

Adding a new launcher

Mixture of Agents (MoA) Integration

What is MoA?

Available presets

serve moa commands

serve moa recommend logic

Creating custom presets

VRAM considerations

See also

Design rules (hardware-neutral, learned this session)

Turbofit v5 — opinionated unified backend

When to use

Quick start

Dynamic Model Database

How it works

What's in the database

Manual usage

Opinionated defaults (turbofit v5)

Catalog schema (v5 extended)

Named flag presets

Tier ladder (used by serve auto)

Commands

How the auto-picker decides

How the scaling ladder works (v1.1 — 3 hardware tiers, universal)

Hardware Tiers (auto-detected by serve auto)

Context-Level Ladder (4 tiers)

Beefy-tier scaling ladder (7 steps, triggered by serve downscale)

API Pairing Matrix

API model rankings (by volume performance)

Pitfalls

mmproj Reference

7. HF download: use exact filenames, never wildcards

8. serve in PATH is Ray's CLI (v5)

2. Per-model binary: is REQUIRED for TurboQuant + NextN models

3. Context floor is enforced everywhere

4. flash_attn is tri-state

5. --gpu is not a valid llama.cpp flag

6. NVIDIA NIM has TWO tiers — free and paid serverless

7. Never create new GitHub repos without explicit user permission

7. The Omni*/Senter* training artifacts and OmniStep are excluded

8. Hermes Desktop → Android APK via Capacitor

Extending turbofit

Removing a model from the catalog

Tier ladder (used by `serve auto`)

Hardware Tiers (auto-detected by `serve auto`)

Beefy-tier scaling ladder (7 steps, triggered by `serve downscale`)

8. `serve` in PATH is Ray's CLI (v5)

2. Per-model `binary:` is REQUIRED for TurboQuant + NextN models

4. `flash_attn` is tri-state

5. `--gpu` is not a valid llama.cpp flag

7. The Omni/Senter training artifacts and OmniStep are excluded

`serve moa` commands

`serve moa recommend` logic

Tier ladder (used by `serve auto`)

Hardware Tiers (auto-detected by `serve auto`)

Beefy-tier scaling ladder (7 steps, triggered by `serve downscale`)

8. `serve` in PATH is Ray's CLI (v5)

2. Per-model `binary:` is REQUIRED for TurboQuant + NextN models

4. `flash_attn` is tri-state

5. `--gpu` is not a valid llama.cpp flag

7. The Omni/Senter training artifacts and OmniStep are excluded

`serve moa` commands

`serve moa recommend` logic