Qwen3.8-27B-Della-Deckard-Fable-Qwopus-ColdFusion-v4-GGUF

Chinese to English Translation of Source Model Card

Three-Stage Construction

⚠️ In this release the retention coefficient $W_{V3} = 0.30$ (not 1.00), so the output is $\text{Qwen3.6} + 0.30 \cdot T(\Delta_{v3}) + 1.00 \cdot \Delta_{\text{ColdFusion}}$. Since $\beta = 1.00$, the base has already landed entirely on Cold-Fusion (Qwen3.8); $\alpha$ only controls how much of the Qwen3.6-side task vector is added. The choice $\alpha = 0.30$ was measured and calibrated, not picked arbitrarily: before construction we measured that Cold-Fusion's displacement relative to official Qwen3.8 is only $\approx 0.7,|W|$ (i.e., in Cold-Fusion − Qwen3.6, 98.7% is generation difference and only 1.3% is its own fine-tuning), while $\Delta_{v3}$ is about $13,|W|$ on the MLPs and nearly orthogonal to it (measured cosine $= +0.005$). With $\alpha = 1.00$, Cold-Fusion's behavior would be drowned out at roughly 15× the volume; with $\alpha = 0.30$, the displacement landing on the MLPs is ≈ 4.6× that of $\delta_{CF}$ (before $T$ truncation).

Recipe (can be handed directly to mergekit)

⚠️ This step is a cross-generation linear transport with no mathematical guarantee. The Qwen3.6 and Qwen3.8 architectures are identical field by field (hidden 5120 / 64 layers / vocab 248320 / FFN 17408 / MTP included / vision tower included), so all shape checks pass green and the model loads — but that does not prove the two generations' weights live in the same base. The measured cold_amp is given in the table below: continued training on the same base should land in 0.02–0.20; if it were close to 1.0, that would mean two independent pre-trainings, and this step would be adding noise.

• stage1-G was never written to disk. Both stages are computed back-to-back within the same row block: first $G = \text{base} + \sum \tilde{\Delta}_i$ is obtained, then $\Delta_G = G - \text{base}$ is immediately used for stage 2. Mathematically identical to writing to disk first and reading it back (fp32 throughout), but it saves ≈54 GB of upload+download.
• ε is auto-narrowed. mergekit requires density ± epsilon ∈ (0,1); in stage 1, 0.85 + 0.30 would be out of bounds and throw an error directly. For this model, ε was narrowed per source up to the legal limit (keeping a 0.02 margin), so the expected densities still equal their respective density values exactly (fable and v2 both have density 0.85 ⇒ ε was narrowed to 0.13 for both). In stage 2 both sources have density 1.00 with no pruning, so ε automatically goes to zero — that part has no randomness at all.

Fusion Health Readings (measured during construction)

Usage

Deviations From the Original Recipe (Supplementary Statement)

• Stage 2 is a pure linear superposition, not DELLA. With density: 1.00, MagPrune degenerates into an identity mapping (no ranking, no Bernoulli sampling, no 1/p rescaling), so stage 2 is strictly equal to $\text{out} = G + 1.00 \cdot (\text{Qwopus} - \text{Qwen/Qwen3.6-27B})$, i.e., mergekit's task_arithmetic. The reason for doing it this way: per-source audits showed Qwopus's increment relative to its ancestor is concentrated in the MLPs and only 0.2–0.8% in magnitude — pruning another 40% off such a small increment would cut signal and leave noise.
• Stage 3 is a cross-generation linear re-anchoring, which is the biggest uncertainty of this model. $\text{out} = \text{Cold-Fusion (based on Qwen3.8)} + (\text{stage2} - \text{Qwen3.6})$ holds only if the two generations' weights live in the same base (i.e., 3.8 was continued from 3.6). The two architectures are identical field by field, so shape checks, loading, and inference will not throw errors — the way it fails is by quietly getting dumber. Measured cold_amp at construction time: 0.1217; the reference range for same-base fine-tuning is 0.02–0.20. If that value were close to 1.0, treat this model as an experiment. Also, Cold-Fusion-GAIN-V1.1 was still at the author-annotated "cook #1 / working title" stage when constructed, and its weights may have since been replaced; specify a commit if you want an exact reproduction.
• No TIES sign election (equivalent to della_linear). The task vectors of v2 and Fable are nearly orthogonal (cos ≈ 0.01); with election on, Fable would be the majority direction and about half of v2's retained elements would be wiped out (measured kill ≈ 13%, exactly the order of half of v2) — effectively projecting away v2's orthogonal features. To preserve it fully, election was disabled this time.