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.
56 lines
2.6 KiB
Bash
56 lines
2.6 KiB
Bash
# Tunables for frank-default-small (batsclamp/Huihui-Qwen3.6-35B-A3B-
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# abliterated-FP8, qwen3_5_moe hybrid MoE, Dankserver, RTX 3090).
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#
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# Runtime: FreeToken v0.1.2 (edge-native MoE serving engine).
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# Installed via uv into /home/dank/freetoken/.venv (CPython 3.12).
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# FreeToken keeps expert weights in host RAM with an LRU GPU cache;
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# KV is paged with radix prefix sharing. Only the 10 full-attention
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# layers page KV (~20 KiB/token); the 30 GDN layers use constant-size
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# per-sequence state (mamba slot pool).
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#
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# Model: batsclamp/Huihui-Qwen3.6-35B-A3B-abliterated-FP8 (HuggingFace
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# safetensors). Abliterated (refusal-removed) Huihui finetune of
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# Qwen3.6-35B-A3B, quantized to the SAME native format the previous
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# official checkpoint used: modelopt block-fp8 e4m3, dynamic
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# activations, 128x128 weight_block_size, bf16 weight_scale_inv;
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# lm_head/embeds/norms/router gates/visual/MTP kept BF16. Un-fused
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# per-expert layout matches the official Qwen FP8, so the FreeToken
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# loader path is identical; ~35 GB on disk. Ships the fixed Qwen3.6
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# chat template (froggeric/Qwen-Fixed-Chat-Templates) — FreeToken
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# reads it from the checkpoint; no template override needed.
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# Replaced Qwen/Qwen3.6-35B-A3B-FP8 on 2026-08-30; pool numbers
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# unchanged — same arch, same weight mass.
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#
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# Pool sizing: GPU budget (~17.6 GiB after weights + fixed caches) is
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# split MoE-first. MOE_CACHE_SIZE pins resident expert slots; NUM_TOKENS
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# pins the KV pool. At 2048 slots / 453005 tokens the split is ~20%
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# residency (2048/10240) and ~8.6 GiB KV. Live resize via
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# `ft ctl cache --kv N --moe M` can grow KV by shrinking MoE without
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# restart (the reverse requires a restart).
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#
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# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE.
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# The ROCm HIP runtime reads those bare env vars (see the 2026-08-13 TEMP
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# incident). This box is NVIDIA/CUDA, but the rule keeps the env portable.
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# Model directory (safetensors checkpoint, ~35 GB, block-fp8).
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MODEL_DIR=/home/dank/models/huihui-qwen36-35b-a3b-abliterated-fp8
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# FreeToken venv (uv-managed CPython 3.12, ft CLI, flashinfer, torch+cu130).
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FT_VENV=/home/dank/freetoken/.venv
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CUDA_HOME_DIR=/home/dank/freetoken/.venv/lib/python3.12/site-packages/nvidia/cu13
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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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# Expert residency: 2048 of 10240 total expert instances (20%).
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# Each slot ~3 MiB → ~6.0 GiB VRAM for the MoE cache.
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MOE_CACHE_SIZE=2048
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# KV pool: 453005 tokens (~8.6 GiB at ~20 KiB/token).
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# Covers 4 concurrent sessions at ~113k unique context each
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# (more with radix prefix sharing for common prefixes).
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NUM_TOKENS=453005
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# Max concurrent decoding requests (also caps mamba slot allocation).
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MAX_RUNNING_REQUESTS=4 |