Lossless 5-bit transformer compression for legal AI
Contract review, e-discovery, due diligence, M&A diligence, regulatory disclosure analysis — all of it requires reproducible model behavior across deploys. AWQ / GPTQ / EXL3 leave reproducibility ambiguous. Sipsa proves SHA-256 verifiable bit-identical reconstruction across 22 architectures — the audit floor for AmLaw 100 + Big-4 deploys.
The legal-AI inference problem
Every BigLaw partner approving an AI-assisted memo wants to be able to answer one question on the next deal: "Is the model that produced this output the same model the firm qualified?"
Current quantization frameworks (AWQ, GPTQ, EXL3, QTIP, SeedLM) deliver "approximately equal to the original" output. Approximately is not good enough for a partner's signature. It's also not good enough when opposing counsel subpoenas the model behind a contested AI-assisted production.
Sipsa's substrate is the only 5-bit-class compression with provable bit-identical reconstruction via SHA-256. Same model, same output, every deploy, cryptographically verifiable. That moves a legal-AI deploy from "approximately reproducible" to "audit-grade reproducible" — the bar that survives partner approval, opposing-counsel discovery, and regulatory examination.
What Sipsa delivers for legal-AI customers
| Need | Sipsa delivery | Compliance hook |
|---|---|---|
| Same model, same output, every deploy | SHA-256 verifiable bit-identical reconstruction | ABA Model Rule 1.1 (competence) + ABA Formal Opinion 512 (generative AI duty of competence) |
| Auditable model versioning for opposing-counsel discovery | Per-Linear SHA-256 manifest + customer-side uc verify | FRCP Rule 26 / Rule 34 production; defensible under expert-witness deposition |
| On-prem deploy (privileged-data customers) | BUSL-1.1 + Additional Use Grant (no cloud dependency); on-prem MSA tier supports air-gapped deploys | Attorney-client privilege preservation; firm's privileged work-product never crosses the Sipsa boundary |
| Smaller GPU footprint per partner desk | 3–4× lower memory at sub-1.5% PPL drift | Lower per-attorney TCO at firm scale |
| Frontier-scale model on partner-grade workstation | Hermes-3-Llama-3.1-405B fits on a single 32 GB consumer GPU | Per-partner research desk economics, no firm-wide datacenter required |
Verified at scale
22 architectures verified end-to-end, 40 model artifacts at huggingface.co/SipsaLabs, customer-side reproducible:
pip install ultracompress hf download SipsaLabs/qwen3-14b-uc-v3-bpw5 --local-dir ./qwen3-14b uc verify ./qwen3-14b # confirms bit-identical reconstruction uc bench ./qwen3-14b # measures TTFT / tokens/sec / VRAM
Phase 0 POC for legal-AI teams ($5K–$25K, 1 week)
We compress one of your production models (or a public model you're evaluating). Deliver the lossless artifact + SHA-256 manifest + customer-side uc verify dashboard. You confirm bit-identical reconstruction against your bf16 reference. If we miss the spec, you don't pay. Phase 1 commercial license follows if Phase 0 lands. Compatible with on-prem / air-gapped law-firm deploys.
FAQ
How does this differ from "fine-tuning Llama on your contract corpus"?
Sipsa is the substrate underneath that fine-tuned model. You can fine-tune any open-weight model (Llama-3.1, Qwen3, Mistral, etc.) on your firm's privileged corpus, then compress the fine-tuned weights through Sipsa's substrate. Result: same fine-tuned behavior, 3–4× lower GPU memory, with cryptographic proof that every partner desk runs the exact-same fine-tuned model.
What about ABA Formal Opinion 512 (duty of competence)?
Opinion 512 requires lawyers to understand the AI tools they use, including the model's training data, capabilities, and limitations. Sipsa's bit-identical reconstruction guarantee makes the "we deployed exactly the model we qualified" disclosure provably defensible under expert-witness review. We can supply the SHA-256 manifest + reconstruction protocol as part of the firm's competence-disclosure file.
What about IP / privilege?
The substrate runs entirely on the customer's machine (on-prem MSA path). No prompts, no documents, no privileged work-product crosses the Sipsa boundary. Substrate code is public on PyPI / GitHub for inspection and audit by firm IT-security review.
Can we use Sipsa for e-discovery review?
Yes — this is one of the strongest fits. E-discovery review at scale requires processing millions of documents per matter. Sipsa lets you fit a 70B-class reasoning model on the GPU you already have, processing 3–4× more documents per GPU-hour, with bit-identical model behavior across every reviewer's desk.