Chimera-1-Qwen2.5-3B

Model X-Ray evidence correction — 6 September 2026. Any structural-location, knowledge-separation, portrait-visualization, lesion-response, legacy simulated-quantization, robustness or prior X-Ray endorsement previously linked from this card has been withdrawn. It is not current evidence. No replacement result is published while validation remains pending. Correction record.

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Model X-Ray is a separate Tetracta service and is not what this card documents. The presence of this repository is not an eligibility grant for it, and nothing measured here is a Model X-Ray result. Its current scope, status and limits: scope and report guide · service status · dated correction record.

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Chimera-1 — two skills, one model, training-free merge (method demo)

Chimera-1 (chimera: in biology, one organism carrying two genetic lineages — which is exactly what this model is) is a proof-of-concept for a simple question: can you take two specialists and stitch their skills into ONE model — with no training at the merge step, and no loss of general ability?

Answer, measured: yes, under conditions we state below.

The headline: +8.8 and +25.0 points on the two target skills in the 1,210-question head-to-head below (the earlier 80-item pilot read +13 and +26), and no measured regression on general ability — Belebele-English, fixed-text perplexity and chat outputs are bit-for-bit indistinguishable from the base. The merge itself is training-free (TIES: task-vector trim + sign election + disjoint mean), runs on CPU in minutes, and the result is a single 3B model with no extra inference cost.

What this is — and is not

• It is a method demo. The two skills (Roman-numeral conversion, character-level word operations) are deliberately simple, verifiable testbeds — chosen because the base model is measurably weak at them (headroom). This is not a production assistant; it is evidence about merging.
• The specialists were made by us with light fine-tuning (top-layers only) — the merge is the training-free part.
• The law we validated across 10 experiments: merge synergy = headroom × complementarity × proximity. If the base already solves the task, there is nothing to gain. If the parents aren't genuinely complementary, merging dilutes. If a parent has drifted far from the shared base (heavy continued-pretraining), aggressive merging produces mush — we show a negative control where a merged coder model drops to 0% on code.
• What we did not manage (stated honestly): three attempts at X-Ray-guided merge surgery (per-layer and per-module weighting/refereeing from internal divergence maps) did not beat uniform TIES on average — one variant preserved the concentrated skill best (95%) at the cost of the other. Blind TIES is a strong baseline. Historical X-Ray interpretation, withdrawn: the original card described internal measurement as predicting profitable pairs (3/3 in our runs) and exposing the trade-off dial. That interpretation is not current evidence or a VG1 capability claim.

Full head-to-head vs the base — 1,210 questions (22 Jul)

Same LL-based harness for both models. Sanity check: the base scores MMLU 67.0%, matching its publicly reported numbers — the harness measures correctly.

Every standard benchmark sits inside the ±2pp bf16 run-noise band (max |Δ| = 1.3pp); the two implanted skills moved +8.8 and +25.0. A capability edit with a measured blast radius of zero — verified on 1,050 standard questions, not assumed from a spot check.

Why we built it

We build an interpretability instrument (an "X-Ray" for LLMs) at tetracta.ai. Model merging is the perfect stress-test for it: everyone merges blind and evals after; we wanted to know how much of the outcome is predictable from the weights, before the merge. Full write-up, per-layer divergence films and the honest failure catalogue: tetracta.ai/research.html.

Use

Qwen2.5 ChatML format, identical to the base model. License inherited from Qwen2.5-3B-Instruct (Qwen Research License — non-commercial, research use).

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("tetracta/Chimera-1-Qwen2.5-3B", dtype="bfloat16")
t = AutoTokenizer.from_pretrained("tetracta/Chimera-1-Qwen2.5-3B")
# try: "Convert the Roman numeral CDXLIV to a regular number." / "Spell the word 'measure' backwards."

— Tetracta AI Teams · for humans, like humans. We measure; we publish the failures too.