hipfire-calib-data

Native BF16-teacher calibration artifacts (.calib.hfq) and fixed KLD reference tensors (.ref_*.bin) produced by the hipfire quantization-quality campaign. One folder per architecture.

Produced on an AMD MI300X (gfx942, CDNA3). All scoring was performed elsewhere (gfx1201); the MI300X was a producer only, because uncalibrated MQ4 is degenerate on gfx942.

.calib.hfq — HFQM calibration package

header 32B: magic "HFQM" | version u32=1 | arch_id u32 | n_entries u32
            | metadata_offset u64 | data_offset u64
metadata:   self-delimited JSON blob
index:      n_entries u32 | per-entry { name_len u16 | name | quant_type u8
            | n_dims u8 | shape[n_dims u32] | group_size u32 | data_size u64 }
payloads:   concatenated, in index order, from data_offset

Each tensor contributes two entries: <name>.hessian and <name>.imatrix. A 496-tensor model therefore has n_entries = 992.

Hessian storage: Bf16TrilDiagF32 (quant_type 130)

Per Hessian, K*4 + K*(K-1)/2 * 2 bytes:

• diagonal — exact f32, K * 4 bytes
• strict lower triangle — bf16, mirrored on read, index i*(i-1)/2 + j for i > j

bf16 conversion is truncation (f32_bits >> 16), not round-to-nearest.

These are full Hessians (XX^T), not diagonals. A 496-tensor 27B package is ~31.5 GB, averaging ~63 MB per Hessian; a diagonal-only package would be ~10 MB total.

REQUIRED: project to PSD before Cholesky

Reading a stored Hessian and running Cholesky directly will fail on roughly half of all tensors. This is measured, not theoretical.

bf16 off-diagonals perturb the Gram matrix out of positive-semidefiniteness by an amount comparable to mean(diag(H)). Measured on layers.1.linear_attn.in_proj_qkv (K=5120) of the Qwen3.8-27B package:

quantity	value
mean(diag(H))	9.951136e-01
off-diagonal RMS	7.889832e-01
Cauchy-Schwarz violations	0 / 1,047,552
leading 1024x1024 lambda_min	-2.088e-01 (353 of 1024 eigenvalues negative)
negative eigenvalue energy	0.99% of positive

Zero Cauchy-Schwarz violations means the data is structurally sound — this is precision noise, not corruption or a normalization error.

Because lambda_min scales roughly as sqrt(K), failure is K-dependent:

K	-lambda_min / mean(diag)	observed GPTQ outcome
5120	~1.0	~50% of tensors fail Cholesky
6144	~1.1	passes only at near-max damping, correction collapses to ~0
17408	~1.8	fails always

Raising the damping cap is not a fix: once damp ~ mean(diag(H)), H + damp*I is diagonal-dominated and GPTQ degenerates to round-to-nearest.

The fix

Eigendecompose, clip negative eigenvalues to zero, rebuild:

evals, evecs = numpy.linalg.eigh(H)
H = (evecs * numpy.clip(evals, 0.0, None)) @ evecs.T
H = (H + H.T) / 2

Measured effect on the tensor above:

	Cholesky succeeds at damp
raw stored	1.0065 (exceeds a 1.0 * mean(diag) cap, so it fails)
PSD-projected	0.01

The projection changes the matrix by 0.1004% (relative Frobenius), which is below bf16's own ~0.391% quantization step. It removes less than the storage format already discarded: denoising, not signal loss. Cost is one O(K^3) eigendecomposition per tensor — the same order as the Cholesky it enables.

.ref_*.bin — KLD reference tensors

Fixed teacher logits for paired KLD/PPL scoring. top-k 256, not full-vocabulary, so absolute KLD values are not comparable to llama.cpp --kl-divergence tables. Relative ranking within this instrument is valid; absolute cross-table comparison is not. Oracle NLL/PPL were computed from full-vocab teacher logits before top-k serialization.

Protocol: n_ctx 2048, 24 chunks, prefill scoring mode.

suffix	corpus
ref_wt2	WikiText-2 — prose. An out-of-distribution tripwire.
ref_v6sel	Held-out rendered chat-template conversations, 0% prose.
ref_ag	AG News.

A prose reference alone is insufficient for a chat model. It compresses arm margins by roughly 3x relative and can invert a PPL ranking. Score against a deployment-distribution reference alongside the prose tripwire.