Finalize model card (accuracy notes) for public release

6edb39d verified 25 days ago

1.7 kB

license: apache-2.0
base_model: BlinkDL/rwkv7-g1
pipeline_tag: text-generation
tags:
  - rwkv
  - rwkv7
  - int4
  - gptq
  - quantized

RWKV-7 G1 7.2B — int4 GPTQ for rwkv-sglang (fits a 16 GB card)

Hand-written weight-only int4 (GPTQ-calibrated, symmetric, group 64) quantization of BlinkDL's RWKV-7 "Goose" G1 7.2B, for the rwkv-sglang serving overlay.

Accuracy: near-lossless at this size. MATH500 avg@64 (500 problems × 64 rollouts): 61.08% vs fp16's 64.18% (−3.1pt) — the same symmetric GPTQ scheme that loses 25.6pt at 1.5B costs three points at 7.2B; bigger models quantize better, measured not assumed. Greedy decoding is token-for-token EXACT on the oracle fixture (8/8); 192/192 projections GPTQ-quantized (streamed Hessian calibration, no RTN fallback).
Runs a 7.2B model on a 16 GB GPU: checkpoint 4.6 GB (vs 14.4 GB fp16); the int4 path serves 7.2B in well under 16 GB total. On a real T4 (16 GB) the sibling RTN build measured greedy 8/8 EXACT, 32.9 tok/s bsz1, peak 6.7 GB.
Speed: int4 decode faster than fp16 at small batch on every tested arch.

Format & loading (important)

Not a drop-in HuggingFace checkpoint. Group-wise (GROUP=64) symmetric int4 (.qweight + .scale); loads only through the rwkv-sglang overlay:

bash scripts/deploy.sh   # from github.com/Hakureirm/rwkv-sglang, onto sglang v0.5.10.post1
RWKV_W4=1 python -m sglang.launch_server --model-path <this-dir> --dtype float16 \
  --trust-remote-code --disable-radix-cache --mem-fraction-static 0.8