Weights
The files are hosted on Hugging Face and pinned to the revisions below (see artifacts in the manifest for hashes). Use calibration.json (per-question-type temperatures) with the option-letter logits.
Calibration temperatures are inherited from the bf16 model, so calibration.json is taken from abhishek085/spark-s1-4b-v6.
This is a decision model, not a chat model: see the usage notes in the card below.
About the JevBench numbers in this card. Figures labelled "JevBench Intelligence" (83.1, 83.2, 82.2) are accuracy on the 231 public items only, mapped to a 0-100 scale. They are not the official JevBench Score. The only official result for these models is for
spark-s1-4b-v6on JevBench v1.6.1: Capability 54.0, Composite 44.9, Intelligence 43.2 (Benchmark Heaven).
spark-s1-4b-v6-nvfp4
NVFP4 (MLP-only) post-training quantization of abhishek085/spark-s1-4b-v6, part of
Open Spark Jev. Only the MLP gate_proj/up_proj/down_proj weights are quantized to NVFP4;
every attention-related module (both the 8 full-attention layers and the 24 linear-attention layers — their conv1d, gating and projection
weights) plus lm_head stay in bf16, since the option-letter logit readout needs full precision there.
This is a very early release; expect it to change quickly.
Speed vs accuracy
Measured on one NVIDIA DGX Spark (GB10), batch size 1, served via vLLM.
| p50 | decisions/s | JevBench Intelligence | |
|---|---|---|---|
spark-s1-4b-v6 (bf16) | 74.9 ms | 13.3 | 83.1 |
spark-s1-4b-v6-nvfp4 |