Instructions to use Hakureirm/rwkv7-sglang-w4gptq-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RWKV
How to use Hakureirm/rwkv7-sglang-w4gptq-1.5b with RWKV:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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 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).
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 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.