clef-omni-int4

INT4 weight-only (W4A16, group size 128, symmetric) quantization of Cloudflare/clef-omni (revision 0db1cd2) made with Intel AutoRound 0.16.0 (SignRound, 200 tuning steps per block on Clef-format records). Only the thinker's MoE experts are quantized; everything else, including the Clef joint decision head, stays BF16.

Status: recommended. Agrees with BF16 on 96.5% of held-out decisions (TV 0.034), at the measurement's noise floor (BF16 vs itself under a different kernel: 95.7%), scores within 0.13 points of BF16 on the Decision Index sample, and scores 56.93 on the full Decision Index 0.2.1 (56.42 on 0.3). It is the only variant that actually saves VRAM today (19.4 GiB of weights) at BF16-like speed (about 0.21 s per decision).

Which Clef-Omni quant should I use?

Model	Format	Size on disk	VRAM (weights)	Agrees with BF16 (top answer)	Decision Index 0.2.1 vs BF16	Best for
Cloudflare/clef-omni	BF16 (original)	70.8 GB	60 GiB	reference	reference (avg 70.3; Cloudflare's table 70.7)	Reference quality, ~70 GB VRAM
dbirks/clef-omni-nvfp4	NVFP4 W4A4 (ModelOpt)	22.0 GB	28 GiB	92.6%	-0.76 pts avg (31/42 within 2 pts)	Blackwell, native FP4 engines
dbirks/clef-omni-int4	INT4 W4A16 g128 (AutoRound)	20.8 GB	19 GiB	96.5%	-0.13 pts avg (33/42 within 2 pts)	⭐ Recommended: best accuracy, smallest VRAM, any recent GPU

Recommended for:

• clef-omni-int4: any recent GPU when you want the smallest VRAM footprint today; loads in plain transformers. Also the best bet on AMD (ROCm): AutoRound's PyTorch/Triton kernels run there, though we have not tested it.
• clef-omni-nvfp4: Blackwell engines with native FP4 tensor cores (W4A4 is the only variant that can run faster than BF16 there).
• clef-omni (Cloudflare BF16): the reference, if you have ~70 GB of VRAM.

Size on disk is the download. VRAM (weights) is what serve_clef.py holds on the GPU: the NVFP4 checkpoints are unpacked to BF16 there (transformers has no packed-NVFP4 kernels), so today only INT4 actually saves VRAM, and the NVFP4 files are for engines with native FP4. Agrees with BF16 is the share of 258 held-out decision questions where the quant picks the same top answer as Cloudflare's BF16 model run through the same code. BF16 against itself under a different expert kernel scores 95.7%, so about 96% is the ceiling. Decision Index is the chance-corrected public index from the Decision Index kit, edition 0.2.1 (the edition behind Cloudflare's published Clef-Omni results), on our hardware.

Quick start

Serve it with serve_clef.py (also in the repo it is maintained in), a single-file PEP 723 script, so uv installs everything it needs:

# uv runs a PEP 723 script straight from its URL (or download serve_clef.py and `uv run` it locally)
uv run https://huggingface.co/dbirks/clef-omni-int4/resolve/main/serve_clef.py --model dbirks/clef-omni-int4 --demo
uv run https://huggingface.co/dbirks/clef-omni-int4/resolve/main/serve_clef.py --model dbirks/clef-omni-int4 --port 8000

The first command answers one built-in decision and exits; the second starts the SystemOne HTTP server (GET /healthz turns 200 once the model is loaded):

curl -s localhost:8000/v1/systemone -H 'Content-Type: application/json' -d '{
  "model": "clef-omni",
  "state": {"observation": "The player is facing a wall."},
  "questions": {"action": {"type": "choice", "criteria": {"left": "Turn left", "right": "Turn right", "forward": "Move forward"}}}
}'

Answers come from Cloudflare's own systemone() in joint_schema_model.py: one forward pass, per-option probabilities from the original BF16 joint decision head. Inputs can include images, audio and video (see the base model card).

How this checkpoint runs

transformers loads this checkpoint natively (AutoRound format). serve_clef.py then re-groups each layer's 128 int4 experts and runs them as one dequantize + grouped matmul per layer (--no-fast-moe turns this off), instead of AutoRound's per-expert Python loop. Weights stay int4 in VRAM (about 19.4 GiB) and Cloudflare's head runs unchanged. Works on non-Blackwell cards.

What was quantized

Part	Precision
Thinker MoE experts	INT4 weights, group size 128, symmetric (auto_round format, auto_gptq packing), BF16 activations
Attention, router, embeddings, lm_head, vision and audio towers	BF16
Clef joint head	BF16, byte-identical to the original

Recipe

AutoRound 0.16.0 scheme=W4A16, iters=200, nsamples=512 Clef-format records fed through Clef's real multimodal forward path, batch_size=1, gradient_accumulate_steps=8, ignore_layers=mlp.gate,lm_head,self_attn; AutoRound's Qwen3-Omni handler unfuses the transformers-5 fused experts into per-expert linears; torch 2.11.0+cu130, transformers 5.10.2; one RTX PRO 6000 Blackwell.

Evaluation

Parity: 128 held-out records (258 questions; text from ultrachat_200k test_sft in Clef's state/questions schema, about 20% with synthetic image/video/audio, never used for calibration). Each exported quant is loaded through serve_clef.py and compared with Cloudflare's BF16 model run through the same code path (grouped experts). We report top-answer agreement and total variation (TV) between the per-option distributions. Noise floor: the same BF16 weights run through a different but equally valid expert kernel (eager vs grouped) agree on only 95.7% of these questions (TV 0.028), because many are near-ties. Agreement around 96% and TV near 0.03 therefore means indistinguishable from BF16 at this precision. Benchmarks: the Decision Index reproduction kit, edition 0.2.1, over /v1/systemone on one RTX PRO 6000 Blackwell.

Benchmarks

Full Decision Index

Full-suite Decision Index for clef-omni-int4 (every scoreable request, coverage 100%, 0 errors): 56.93 on edition 0.2.1 (raw 66.87; areas: knowledge 49.96, language 54.48, retrieval 58.92, tools 73.38, arts 47.27) and 56.42 on the live 0.3 board (raw 66.84). For orientation on the public board: Cloudflare Clef (Qwen3.8-27B base) 61.21 / 61.71, Jev 57.91 / 57.89, Clef-flash 57.07 (0.2.1). Cloudflare publishes per-benchmark Clef-Omni scores but no Clef-Omni index, so there is no BF16 index to compare directly; the paired 3,000-request sample puts INT4 within 0.13 points of BF16. Run with serve_clef.py, which uses Cloudflare's encode_record (it truncates state beyond 64K tokens, where the board's rule counts such rows unsupported).

Paired sample vs BF16

Decision Index 0.2.1 (the edition behind Cloudflare's published Clef-Omni table), run with the public decision-index kit (suite rebuilt and hash-verified against the lab's manifest) over /v1/systemone on one RTX PRO 6000 Blackwell. We score a deterministic stratified sample of 3,000 requests (about 58 per benchmark, suite sample --n 3000) rather than all ~150K, so each benchmark carries roughly ±5-7 points of sampling noise; the averages and the paired deltas (both models on the identical questions, score --reference) are the reliable signals. The kit's headline index is coverage-adjusted over the full suite, so it is not meaningful on a sample and is not reported.

Benchmark (metric)	n	Cloudflare BF16 (published)	BF16 (ours)	This quant	Δ vs BF16 (same questions)
BFCL (case exact accuracy)	59	98.2	100.0	100.0	+0.0
ToolRet (nDCG@10)	59	66.6	66.5	67.5	+1.0
API-Bank (accuracy)	59	92.7	79.7	81.4	+1.7
BANKING77 (macro-F1)	59	94.8	96.5	96.5	+0.0
CLINC150+OOS (macro-F1)	59	97.7	94.8	94.8	+0.0
RouterBench (selected quality (quality objective))	118	79.9	78.8	78.8	+0.0
Home appliance simulator (case exact accuracy)	59	69.3	67.8	74.6	+6.8
SGD/SGD-X (macro-F1)	59	39.2	34.2	31.8	-2.3
ContractNLI (macro-F1)	59	80.3	79.4	78.5	-1.0
ANLI (macro-F1)	59	65.5	71.4	71.4	+0.0
BPoMP (accuracy)	246	96.8	98.4	98.0	-0.4
Humicroedit (accuracy)	59	70.1	64.4	62.7	-1.7
POP909-CL (accuracy)	59	2.6	1.7	1.7	+0.0
cfcolor (accuracy)	59	62.9	61.0	62.7	+1.7
MMLU (accuracy)	59	92.7	91.5	91.5	+0.0
GPQA Diamond (accuracy)	59	47.4	52.5	47.5	-5.1
ARC-Easy (accuracy)	59	99.8	96.6	96.6	+0.0
ARC-Challenge (accuracy)	59	97.8	96.6	96.6	+0.0
WinoGrande (accuracy)	59	96.7	100.0	100.0	+0.0
HellaSwag (accuracy)	59	99.1	94.9	94.9	+0.0
GSM8K (accuracy)	118	79.0	78.0	77.1	-0.8
ChessBench (accuracy)	58	22.1	17.2	17.2	+0.0
MuSR (accuracy)	58	83.8	82.8	89.7	+6.9
SATA-Bench (case exact accuracy)	58	31.9	31.0	31.0	+0.0
SimpleBench (accuracy)	10	n/a	40.0	40.0	+0.0
BRIGHT (nDCG@10)	58	42.0	38.0	37.3	-0.6
Amazon ESCI (macro-F1)	58	57.8	53.2	53.2	+0.0
ACOS (per-review F1)	228	28.4	26.1	26.2	+0.0
FinEntity (macro-F1)	58	96.3	97.5	96.7	-0.8
VAST (macro-F1)	58	50.9	59.3	59.2	-0.1
NLI4CT (macro-F1)	58	76.8	80.2	84.2	+4.1
CRUXEval (accuracy)	58	88.8	82.8	84.5	+1.7
CLadder (accuracy)	58	97.2	98.3	98.3	+0.0
HLE (accuracy)	58	n/a	13.8	13.8	+0.0
ForecastBench (Brier (lower is better))	58	11.7	14.3	14.3	-0.1
Habermas Machine (accuracy)	58	67.8	67.2	67.2	+0.0
PhishNChips phishing decisions (accuracy)	58	73.2	79.3	72.4	-6.9
MMLU-Pro (accuracy)	58	70.6	74.1	75.9	+1.7
BBH fixed-option tasks (accuracy)	58	69.0	69.0	67.2	-1.7
RAGTruth response-level hallucination (F1 on hallucinated class)	58	42.0	56.2	51.6	-4.6
HoVer claim verification (accuracy)	58	64.2	67.2	63.8	-3.4
When2Call MCQ (accuracy)	58	63.3	56.9	53.4	-3.4
New Yorker caption matching (accuracy)	58	64.2	67.2	69.0	+1.7

Version history

Each version is an annotated git tag on this repo (revision="v0.1" etc. pins it). Newest last.

Version	Date	Notes
v0.1	2026-10-10	First release: AutoRound 0.16.0 INT4 W4A16 g128, experts-only, 200 SignRound steps per block on 512 Clef-format records (113 min on one RTX PRO 6000). Export graded through serve_clef.py: 95.7% top-answer agreement with BF16 (mean TV 0.033), 19.4 GiB weights in VRAM.
v0.2	2026-10-10	Docs and script only, weights unchanged: serve_clef.py now regroups the 128 int4 experts per layer and runs them as dequant + grouped matmul, so a decision takes 212 ms instead of 2,689 ms (median, RTX PRO 6000) with the same answers (96.1% top-answer agreement with BF16 on the exported checkpoint) and 19.4 GiB of weights in VRAM.
v0.3	2026-10-10	Made public. README only: quant comparison table now links the public NVFP4 sibling, with parity measured on the exported files; weights and serve_clef.py unchanged from v0.2.
v0.4	2026-10-10	README only: Decision Index 0.2.1 results on a 3,000-request stratified sample. INT4 is within -0.13 points of BF16 on average (paired, 42 benchmarks; 33 within 2 points), and our BF16 run reproduces Cloudflare's published table within 0.4 points (70.3 vs 70.7 over 38 benchmarks).
v0.5	2026-10-11	README only: parity re-measured against a same-path BF16 reference (96.5% top-1, TV 0.034; BF16 noise floor 95.7%), comparison table updated with the public NVFP4 benchmarks; serve_clef.py now also loads FP8 and AutoRound NVFP4 checkpoints and keeps NVFP4 experts packed (about 28 GB instead of about 60 GB).
v0.6	2026-10-12	README only: full-suite Decision Index (every request, 0 errors): 56.93 on edition 0.2.1 (raw 66.87) and 56.42 on 0.3, with per-area scores and board context.

License

Apache-2.0, as the base model. All credit for the model goes to Cloudflare; this repo only changes the weight precision.