Frank
4d5bea42e0
Two live-verified loader failures on Dankserver (2026-09-01), each a
clean exit at load (no swap-thrash, 37 GB fits RAM):
1. ornith-ai/Ornith-1.5-35B-A3B-FP8 (compressed-tensors PER-CHANNEL fp8):
ValueError: Missing MoE expert source layers: {'gate_up': [0, 1, ...]}
— nonstandard expert tensor naming, provider finds no expert sources.
2. lamianlbe/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-fp8
(compressed-tensors BLOCK-fp8 128x128, standard per-expert naming):
RuntimeError: The size of tensor a (2048) must match the size of
tensor b (...) in _dequant_fp8_weight — the dequant path multiplies
weight * weight_scale directly and cannot expand compressed-tensors
block scales.
Empirical matrix for FreeToken v0.1.2 on qwen3_5_moe (RTX 3090):
modelopt block-fp8 (quant_method 'fp8' + weight_scale_inv) -> LOADS
compressed-tensors per-channel FP8 -> fails
compressed-tensors block-fp8 128x128 -> fails
batsclamp/Huihui-...-abliterated-FP8 is the proven uncensored checkpoint;
restoring it until a modelopt/NVFP4 requant of a wanted model exists or
FreeToken learns compressed-tensors.
77 lines
2.9 KiB
Bash
Executable File
77 lines
2.9 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Serve batsclamp/Huihui-Qwen3.6-35B-A3B-abliterated-FP8 as
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# frank-default-small via FreeToken on Dankserver (RTX 3090, 60 GB RAM).
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#
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# Runtime: FreeToken v0.1.2 (edge-native MoE serving engine).
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# - Experts live in host RAM; an LRU cache of expert slots is resident
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# on the GPU. Misses are streamed over PCIe.
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# - Paged KV cache (radix prefix sharing). Only the 10 full-attention
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# layers page KV (~20 KiB/token); the 30 GDN/linear-attention layers
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# hold constant-size per-sequence state (mamba slots).
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# - OpenAI-compatible API (/v1/chat/completions, /v1/models).
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#
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# Model: batsclamp/Huihui-Qwen3.6-35B-A3B-abliterated-FP8 — Huihui
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# abliterated finetune of Qwen3.6-35B-A3B in the same native modelopt
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# block-fp8 format (e4m3, 128x128 weight_block_size, un-fused per-expert
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# layout) as the official FP8 checkpoint this service ran before; the
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# FreeToken loader path is unchanged. 40 layers (10 attention + 30
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# GDN), 256 experts / 8 used, 2 KV heads × 256 dim. ~35 GB on disk.
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# The checkpoint ships the fixed Qwen3.6 chat template; FreeToken reads
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# it from the model directory.
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#
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# Pool sizing: the GPU budget is split MoE-first between expert residency
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# and KV pages. --moe-cache-size pins the number of resident expert
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# slots; --num-tokens pins the KV pool capacity. Live resize is possible
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# via `ft ctl cache --kv N --moe M` (grows KV by shrinking MoE; shrinking
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# KV requires a restart due to the swap-during-rebuild memory cost).
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#
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# Tunables come from frank-default-small.env unless FRANK_SMALL_ENV_FILE
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# overrides that path.
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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ENV_FILE="${FRANK_SMALL_ENV_FILE:-$SCRIPT_DIR/frank-default-small.env}"
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if [[ -f "$ENV_FILE" ]]; then
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set -a
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# shellcheck disable=SC1090
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source "$ENV_FILE"
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set +a
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fi
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# --- defaults (used when the env file does not set them) -----------------
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: "${MODEL_DIR:=/home/dank/models/huihui-qwen36-35b-a3b-abliterated-fp8}"
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: "${ALIAS:=frank-default-small}"
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: "${BIND_ADDR:=0.0.0.0}"
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: "${LISTEN_PORT:=7770}"
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: "${FT_VENV:=/home/dank/freetoken/.venv}"
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: "${CUDA_HOME_DIR:=${FT_VENV}/lib/python3.12/site-packages/nvidia/cu13}"
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# Pool sizing (expert residency vs KV cache tradeoff).
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: "${MOE_CACHE_SIZE:=2048}"
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: "${NUM_TOKENS:=453005}"
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: "${MAX_RUNNING_REQUESTS:=4}"
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# Paths
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FT_BIN="${FT_VENV}/bin/ft"
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MODEL="${MODEL_DIR}"
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if [[ ! -x "$FT_BIN" ]]; then
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echo "missing FreeToken CLI: $FT_BIN" >&2
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exit 1
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fi
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if [[ ! -d "$MODEL" ]]; then
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echo "missing model directory: $MODEL" >&2
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exit 1
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fi
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export PATH="${FT_VENV}/bin:${PATH}"
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export CUDA_HOME="${CUDA_HOME_DIR}"
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exec "$FT_BIN" serve \
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--model "$MODEL" \
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--served-model-name "$ALIAS" \
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--host "$BIND_ADDR" \
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--port "$LISTEN_PORT" \
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--moe-cache-size "$MOE_CACHE_SIZE" \
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--num-tokens "$NUM_TOKENS" \
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--max-running-requests "$MAX_RUNNING_REQUESTS" |