Frank
219a097b39
Every payload swap a live endpoint has ever run is now one row in that endpoint's Eras table, with its rollback path in the same row. No per-attempt page, no per-swap ledger, nowhere else. - Added: LLM/frank-archon.md (radiance, 2x R9700 TP=2), LLM/frank-dragoon.md (halogen flash-server, 19 eras), LLM/frank-zealot.md (llama.cpp PrismML fork on the 3090). Each carries Current shape, Eras, Laws, wire contract, files. - Deleted: 45 per-payload runbooks -- the frank-dragoon line (11 including the pre-name halogen/kingjones/kairic/CIRU/eemin/agentionai/dflash2 pages and the benchmark page), the frank-zealot line (llama.cpp/vLLM/ik_llama/SGLang eras), the frank-archon line (vLLM/FreeToken/R9V/llama.cpp/radiance eras), plus frank-medium, frank-npu and the ubatch/PCIe one-offs that owned nothing. - Rewired: every wikilink and markdown-path reference to a folded page points at the endpoint page instead; the three Research see-also lists and the ubatch sweep's runbook link included. No anchored links existed in the set. - Regenerated both index pages as pure locators -- one row, one clause, no numbers -- so the docs_completeness locator law holds for all 153 pages. tests/docs_completeness.bats green: index links every page, no unresolved links. The pages moved to .AGENTTRASH/20261018-doc-dedupe/LLM/ (gitignored, and git history holds every byte).
230 lines
10 KiB
Markdown
230 lines
10 KiB
Markdown
---
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type: reference
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description: "This page records the full redo of the inference fleet on 2026-08-17."
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tags:
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- llm
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- frank
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timestamp: 2026-09-03
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---
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# Frank Program Redo — 2026-08-17
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This page records the full redo of the inference fleet on 2026-08-17.
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The pipeline-parallel split and the FastFlowLM NPU endpoint were retired.
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The old scripts moved to `old/scripts/` in this repository.
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> **HISTORY — the topology below is two fleet generations dead.** Today the
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> fleet is three endpoints: [[frank-archon]]
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> (`192.168.0.235:7770`, radiance 1.0.13, 2×R9700 TP=2),
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> [[frank-dragoon]] (`192.168.0.143:7770`, halogen
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> flash-server 0.16.2 on the iGPU) and
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> [[frank-zealot]] (`192.168.0.143:7771`, llama.cpp
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> PrismML fork build 10754 on the RTX 3090 — the card that sat in Dankserver
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> until 2026-09-15). No vLLM and no ROCm endpoint serves anything; there is no
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> pipeline parallelism and no NPU endpoint.
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## New Topology
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| Service | Host | Runtime | Model | Port |
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|---------|------|---------|-------|------|
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| frank-dragoon | Dankserver | llama.cpp (patched FastMTP) | HauhauCS Qwen3.8-27B-Uncensored-Aggressive IQ4_XS | 7770 |
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| frank-zealot | Dankclaw | vLLM ROCm | nameistoken Qwen3.6-35B-A3B Quark W8A8 INT8 | 52625 |
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Names as the 2026-08-17 commit wrote them: `frank-default-large` on Dankserver
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(`git show d9af7f1:scripts/frank-default-large.env:20,22` → `ALIAS=frank-default-large`,
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`LISTEN_PORT=7770`) and `frank-default-small` on Dankclaw
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(`git show d9af7f1:scripts/frank-default-small.env:16,18` → `ALIAS=frank-default-small`,
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`LISTEN_PORT=52625`); the Protoss ids date from 2026-10-06 → 10-10.
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The RTX 3090 runs the whole 27B model standalone. No RPC worker, no
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tensor split, no NPU.
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## Models
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### frank-dragoon — HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF:IQ4_XS
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- Base: Qwen3.8-27B (dense 27B, 64 layers: 48 GatedDeltaNet + 16
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gated-attention, hidden 5120, 24 Q heads / 4 KV heads, head dim 256,
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native 262144 ctx).
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- Quant: IQ4_XS (4.60 BPW, 15.71 GB). Every GGUF in the repo preserves the
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native embedded MTP/NextN head.
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- FastMTP draft sidecar: `...FastMTP-32K.gguf` (903 MB). Requires the
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patched llama.cpp build (commit `4df29be4f4c3673f428170fda944a5b19f743bb8`
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+ `HauhauCS-FastMTP-llama.cpp.patch`). Without the patch, the draft load
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fails with `expected 5120, 248320, got 5120, 32768`.
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- Spec chain: FastMTP (`--spec-type draft-mtp`, `--spec-draft-n-max 2`,
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`--spec-draft-ngl all`, `--spec-draft-p-min 0`) then ngram-mod fallback
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(48/64/24).
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### frank-zealot — nameistoken/Qwen3.6-35B-A3B-Quark-W8A8-INT8
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- Base: Qwen3.6-35B-A3B (hybrid MoE, 256 experts top-8, ~3B active, 40
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layers: 10 blocks of 3 DeltaNet + 1 Attention, native 262144 ctx).
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- Quant: AMD Quark W8A8 INT8 (per-channel static weights, per-token
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dynamic activations). MoE experts stored as per-expert nn.Linear
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triplets that match vLLM's FusedMoE mapping exactly. Weights ~35 GB;
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the vision tower, router, lm_head stay BF16.
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- Served with vLLM ROCm, ngram speculative decoding, and tool calling.
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- Swap history: the initial deployment used
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`Avesed/Qwen3.6-35B-A3B-INT8-W8A8` (compressed-tensors INT8). Its tool
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calls were malformed (the model emitted `<function=get_weather(city=...)>`
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instead of the Qwen3 XML structure). The Quark quant follows the format
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cleanly; swapped in the evening of 2026-08-17.
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## Context Math (frank-dragoon, RTX 3090)
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KV per token: 16 attention layers x 4 KV heads x 256 dim.
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K q8_0 = 1088 B per layer (4 KV heads × 256 dim × 1.0625 B), V f16 = 2048 B per
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layer, ×16 layers = 50176 B/token total.
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Budget (MiB): 24576 total - 14982 weights - 861 draft - 1024 fixed
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(CUDA context, compute graph, scratch, draft KV) - headroom.
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| Context | KV (MiB) | Free after load |
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|---------|----------|-----------------|
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| 114688 (112K) | 5488 | 2221 (2.17 GiB) |
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| 122880 (120K) | 5880 | 1829 (1.79 GiB) |
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| 131072 (128K) | 6272 | 1437 (1.40 GiB) |
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| 147456 (144K) | 7056 | 653 (0.64 GiB) |
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Production choice: **131072 (128K)** — the maximum that keeps about
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1.4 GiB headroom for checkpoints. Lower `CTX_SIZE` in
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`scripts/frank-dragoon.env` to 122880 for ~1.8 GiB headroom.
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## Install Runbook
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### Dankserver (frank-dragoon)
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1. Stop the old worker: `sudo systemctl stop frank-pp-worker`.
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2. Download the model and draft to `/home/dank/models/hauhaucs-qwen38/`.
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3. Build the patched llama.cpp:
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```
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git clone https://github.com/ggerganov/llama.cpp /home/dank/llama.cpp-fastmtp
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cd /home/dank/llama.cpp-fastmtp
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git checkout 4df29be4f4c3673f428170fda944a5b19f743bb8
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curl -L -o HauhauCS-FastMTP-llama.cpp.patch <HF patch URL>
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git apply HauhauCS-FastMTP-llama.cpp.patch
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cmake -S . -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release \
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-DGGML_CUDA_FA=ON -DGGML_CUDA_FA_ALL_QUANTS=ON
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cmake --build build --config Release -j$(nproc)
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```
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`GGML_CUDA_FA_ALL_QUANTS=ON` is mandatory. Without it, prefill collapses
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~12x (the q8_0 KV + flash attention falls back to a slow path;
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measured 95 t/s vs 1300+ t/s at 8K on the 3090).
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4. Symlink the unit, reload, enable, restart:
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```
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sudo ln -sf /home/dank/vLLM/scripts/frank-dragoon.service /etc/systemd/system/
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sudo systemctl daemon-reload && sudo systemctl enable frank-dragoon
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sudo systemctl restart frank-dragoon
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```
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5. Verify: `curl -sf http://127.0.0.1:7770/health` and a chat completion.
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6. Confirm VRAM headroom with `nvidia-smi`.
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### Dankclaw (frank-zealot)
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1. Stop the old services: `sudo systemctl stop frank-dragoon
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frank-zealot` (retired PP head and NPU endpoint).
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2. Install uv and create the venv:
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```
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curl -LsSf https://astral.sh/uv/install.sh | sh
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uv venv --python /usr/bin/python3.12 /home/dank/vllm-venv
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uv pip install --python /home/dank/vllm-venv vllm --torch-backend=auto
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```
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The resolver pulls the AMD ROCm wheel set (torch +
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amd-torch-device-gfx1151 + the vllm rocm variant) from the AMD index.
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3. Cache the model (default HF cache):
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```
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python3 -c "from huggingface_hub import snapshot_download; snapshot_download('Avesed/Qwen3.6-35B-A3B-INT8-W8A8')"
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```
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4. Install the ROCm wheel set and the flash-attention triton path:
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```
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uv pip install torch --torch-backend=auto
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uv pip install "vllm==0.27.1+rocm723" --index-url https://wheels.vllm.ai/rocm/ \
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--extra-index-url https://pypi.org/simple --index-strategy unsafe-best-match --no-build-isolation
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cp -r <ciru-venv>/site-packages/flash_attn* <vllm-venv>/site-packages/
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# add a flash_attn_2_cuda stub module (ROCm build has no compiled .so)
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```
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Set `FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE` (env file). Without it the
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vision encoder falls back to torch SDPA and engine init OOMs.
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5. Symlink the unit, reload, enable, restart:
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```
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sudo ln -sf /home/dank/vllmScripts/scripts/frank-zealot.service /etc/systemd/system/
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sudo systemctl daemon-reload && sudo systemctl enable frank-zealot
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sudo systemctl restart frank-zealot
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```
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6. Verify: `curl -sf http://127.0.0.1:52625/health` and a chat completion.
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## Fallback: vLLM source build for gfx1151
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If the prebuilt ROCm vLLM wheel cannot run on gfx1151, build from source.
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The retired CIRU package documents the proven recipe:
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`/home/dank/ling3-ciru/Ling-3.0-Flash-CIRU-int4-Strix-native/scripts/build-vllm-gfx1151.sh`
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(branch `ciru/ling3-gfx1151-rocm715`, index `https://rocm.nightlies.amd.com/whl-multi-arch/`).
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The upstream starting point needed a five-line ROCm sampler fix to launch
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on gfx1151; newer vLLM releases may already include it.
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## Configuration
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- `models.json` (pi): two providers — `llamacpp (dankserver)` at
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http://192.168.0.235:7770/v1 and `vllm (dankclaw)` at
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http://192.168.0.143:52625/v1. (The harness config today has three providers
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and no vLLM: `~/.omp/agent/models.yml:14,94,173`.)
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- HF token: `~/.cache/huggingface/token` on both machines (mode 600).
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- OpenWebUI (sibling repo): retarget `OPENAI_API_BASE_URL` to the
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Dankserver endpoint.
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## Evening Session (2026-08-17): tool calling, model swap, batch 8192
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Three changes landed after the initial deployment:
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### 1. Tool calling on frank-zealot (HTTP 400 fix)
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Clients (pi) send `tool_choice: "auto"`. vLLM rejects it with HTTP 400
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unless the server enables tool calling. Fixes applied:
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- `--enable-auto-tool-choice --tool-call-parser qwen3_xml` (the Avesed /
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Quark chat template renders the Qwen3 XML tool format, and qwen3_xml
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is the matching parser).
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- Custom chat template `/home/dank/models/qwen36-chat_template-tools.jinja`:
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derived from the repo's `chat_template.jinja` (the tokenizer_config.json
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ships no template, so vLLM would use its built-in one, which does not
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render the XML tool instructions). The custom template adds:
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- a concrete WORKED EXAMPLE built from the actual tool schema
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(name + parameter keys) — models follow concrete examples much
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better than the generic placeholder;
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- tool requests default to non-thinking (`enable_thinking` unset + tools
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present -> empty think block). Qwen3 tool calls only work reliably
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with thinking off; normal requests keep thinking.
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Known pitfall: the earlier Avesed model still emitted malformed XML even
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with the strong template (`<function=get_weather(city=...)>`); that was
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model behavior, which motivated the swap.
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### 2. Model swap: Avesed INT8 W8A8 -> Quark W8A8 INT8
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`frank-zealot` now serves
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`nameistoken/Qwen3.6-35B-A3B-Quark-W8A8-INT8` (AMD Quark 0.11.1, ~35 GB,
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vLLM-native expert layout). The Avesed compressed-tensors quant's
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Qwen3-XML tool fidelity was poor. Verified with the Quark model: plain
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tool requests (no chat_template_kwargs) return clean
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`tool_calls: [{"name": "get_weather", "arguments": {"city": "Paris"}}]`;
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non-tool requests keep reasoning. The Avesed checkpoint stays in the HF
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cache for rollback.
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### 3. Batch 8192
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`MAX_NUM_BATCHED_TOKENS` lowered from 16384 to 8192 (user request).
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vLLM has no separate ubatch knob; the scheduler issues one batch per
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step up to this token cap.
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### 4. Prefix caching enabled
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Added `--enable-prefix-caching`. The model is hybrid (GatedDeltaNet +
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full attention); vLLM keeps prefix caching **opt-in** for hybrid models
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(`default_prefix_caching = is_prefix_caching_supported and not
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is_hybrid`), so it was off by default. Enabling it reuses the KV of
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repeated prompt prefixes. Measured on a 2023-token prefix (2026-08-17
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evening): 4.6s cold -> 1.1s cached (~4x TTFT), output unchanged. This is
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the main TTFT lever for pi, which re-sends the same system prompt + tool
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schema every turn. Toggled via `ENABLE_PREFIX_CACHING` in the env file.
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