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

# RWKV-7 G1 1.5B — int4 GPTQ for rwkv-sglang

Hand-written **weight-only int4** (GPTQ-calibrated, symmetric, group 64) quantization of
BlinkDL's RWKV-7 "Goose" G1 1.5B, for the
[rwkv-sglang](https://github.com/Hakureirm/rwkv-sglang) serving overlay.

- **Accuracy (perplexity-style):** GPTQ (wikitext-calibrated) lambada 0.639 vs 0.672 fp16
  (−3.34pt), recovering +1.6pt over calibration-free RTN; kernel output is bit-identical to
  the offline dequant.
- **Speed:** faster than fp16 at every batch size ≤ 32 on an RTX 3090 (1.03–1.56× decode),
  via a hand-written int4 GEMV / small-M GEMM / tensor-core GEMM family (JIT, Turing→Blackwell).
- **VRAM:** checkpoint 1.2 GB vs 2.9 GB fp16 (~2.4×); serve VRAM −950 MiB at bsz1.

## ⚠️ Accuracy warning — multi-step reasoning (read before use)

Perplexity-style metrics understate int4's damage to multi-step reasoning **at this model
size**. On MATH500 (avg@64, 32,000 rollouts) this checkpoint scores **14.98% vs fp16's
40.60% (−25.6pt)** — the quantized model tends to lose the thread mid-derivation and run to
the token cap (57.7% truncation vs fp16's 14.2%). This is a 1.5B-specific fragility, not a
property of the scheme itself: the identical symmetric GPTQ at 7.2B costs only −3.1pt on the
same ruler (see [rwkv7-sglang-w4gptq-7.2b](https://huggingface.co/Hakureirm/rwkv7-sglang-w4gptq-7.2b)).

**Recommendation:** treat this checkpoint as a memory-footprint tool for non-reasoning
workloads. For reasoning-heavy use at 1.5B, use the int8 w8g64 tier instead (greedy-exact,
no measurable accuracy cost) — int4 is not the lossless tier at this size.

## Format & loading (important)
Not a drop-in HuggingFace checkpoint. Weights are group-wise (GROUP=64) symmetric int4
(`.qweight` + `.scale`); they load **only** through the rwkv-sglang overlay:

```bash
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
```

LoRA/norm/embedding/head stay full precision. Base model © BlinkDL (Bo Peng), Apache-2.0.