Cassandra T1.5 — actual model outputs
Five prompts. Three model checkpoints. Real captured outputs from the eval pipeline at data/cassandra-t1.5-eval.json. This is the published methodology applied to retrieval-grounded fine-tuning under anchor-token masking — measured pooled corpus overlap improved ~60× across the continuation runs.
Measured metrics · all 12 eval queries
Corpus overlap improves 60× across the continuation runs
v1 base · epoch 5
before anchor-token continuation
+500 steps · anchor-token
continued training under anchor-token masking
+1000 steps · anchor-token
extended continuation under anchor-token masking
Pooled corpus overlap measures the fraction of tokens in the model's output that come from the retrieval window seeded for the prompt — a direct measure of whether the model is grounding its response in the provided context vs. hallucinating from prior. v1_base scores 0.008 (essentially zero grounding); continued_step500 jumps to 0.481 — the anchor-token training is teaching the model to read its retrieval window. Pooled English ratio measures fraction of output tokens that are valid English. Slight drop in continuation runs because the model is correctly reproducing technical/named-entity tokens from the corpus (lower English-dictionary hit rate, higher actual signal).
Per-prompt outputs · captured from eval pipeline
Pick a prompt — see all three checkpoints side-by-side
Active prompt
“The cold storage workflow…”
v1 base · epoch 5
before anchor-token continuation
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+500 steps · anchor-token
continued training under anchor-token masking
The standard T 1 includes a standard which includes customer customer calling , one calling the customer calling , , the customer calling customer calling , , , the customer the customer calling in , and one and the customer customer calling three up . The cost which will cost the business is $ 4 20 0 in . .
+1000 steps · anchor-token
extended continuation under anchor-token masking
The standard T 1 uses a a customer with the the business , the business signing on , the business signing in on , the business signing up on on , the customer signing in , , the business signing it on . . The Cold _ ep County . . is used for roughly 18 hours . .
Outputs are raw — no post-processing, no cherry-picking. Notice how v1_base produces token soup on retrieval-grounded prompts; the continuation runs begin to read from their retrieval window. The remaining repetition in step500/step1000 is real and not hidden — it's exactly the signal we're training against next.
Second architecture · V2 scratch run
T1 epoch 5 vs V2 architecture step 80,000
A second architecture was trained from scratch alongside T1 to test convergence speed and output structure. After 80,000 steps V2 reached training loss ≈2.2 — comparable to T1 at epoch 5 (loss 2.26) — but with a distinctly different failure mode (mode-collapse on directional tokens rather than high-entropy soup). This is the kind of architectural exploration the funding ask continues. Raw output captured under a fixed 10-prompt set; nothing cherry-picked.
Active prompt #1
“Who are you?”
T1 · epoch 5
loss 2.26 · first architecture · 1.3B params
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V2 · step 80,000
loss ≈2.2 · second architecture · from-scratch run
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T1 failure mode
High-entropy token soup. The model has wide vocabulary distribution but no syntactic anchoring — outputs read like a scrambled dictionary. The 1.67× anchor-token advantage was developed against exactly this failure mode.
V2 failure mode
Directional-token mode collapse. Outputs lock onto repeating “left/right” tokens — a different pathology that suggests V2's position-encoding choice is over-biasing toward spatial schema in the training corpus. Useful negative result; informs T1.6 architecture.
Raw file: cassandra-v2-vs-t1-raw.txt · GitHub source: eval/v2-ep2-vs-t1-ep5-coherence-raw.txt
What funding extends to
Cassandra T1.6 — architecture redesign + full training run
Architecture
T1.5 is a 5-epoch test bed for the anchor-token masking methodology. T1.6 redesigns the layer stack for faster training convergence + better output quality. Architecture work is complete; needs compute.
Training compute
Bench-grade GPU available; full training requires multi-GPU cluster time. Cost is bounded and quantifiable — a clear ask, not open-ended R&D.
Evaluation suite
Beyond pooled corpus overlap: standard suites (TinyMMLU, HellaSwag-100, custom service-domain QA) will be run + published with the same level of methodological honesty shown on this page.
Fund Cassandra T1.6 training
The research is published (Zenodo DOI 10.5281/zenodo.20496783). The test-bed model is on GitHub (Apache 2.0). The captured outputs above prove the methodology produces measurable corpus-grounding gains. Funding extends from the bench rig to a full training run + standard public evaluation suite.