Q-Invoice-50M-Sovereign — Invoice JSON extractor — totals, currency, line counts

Invoice JSON extractor — totals, currency, line counts

What this model does, in one sentence

Given an invoice or purchase order body, returns a JSON object with the requested fields (e.g. total_value, currency, line_count, vendor, due_date). Outputs strictly conform to the requested schema.

Honest performance

• Task: invoice extraction
• Metric: json_content (extracted JSON object equals gold (canonicalized))
• Holdout: n=60 rows, never seen in training, scored row-by-row
• Score: 100.0% mean
• Bootstrap CI 95% lower bound: 1.000
• Gate threshold: 0.95
• Verdict: PASS at point estimate AND at bootstrap CI lower bound

What it's used for — real workflows

(see Example below)

What problem this actually solves

See use cases above.

Integration paths

(call the model directly via the Python snippet below)

Example

Input:

Extract invoice total. JSON {total_value, currency}.
INV-7777
Net: 88.10
VAT: 17.62
Total: 105.72 EUR

Output:

{"total_value": 105.72, "currency": "EUR"}

What this is NOT

• Not a general-purpose chatbot. This head does one job and does it consistently. Free-text generation outside the trained task surface will degrade.
• Not a replacement for a verifier. This is one component in the Qovaryx cluster-shell architecture. The decision-acceptance discipline lives in the wrapper, not in the head.
• Not reproducible from this card. Weights and audit are public; the crystal corpus, eval gate constants, and training hyperparameters are not.

Proprietary Qovaryx technology — built on our own scratch base

This is a 53.5M-parameter sovereign specialist in the Qovaryx Compact Specialist Suite. It is full-fine-tuned from tjarvis91/qovaryx-50m-scratch-base — our own scratch-trained base, not a borrowed foundation model.

• Base: Qovaryx 50M scratch base. Pretrained from random initialization on 491.5M tokens. Not SmolLM2. Not Qwen. Not Llama. Not Mistral. Not Phi. No HuggingFace foundation. No closed-source weights. Every parameter traces back to a Qovaryx training run on Qovaryx hardware.
• Tokenizer: Qovaryx english_v1 BPE (vocab 32000), built in-house against our own pretraining corpus.
• Architecture: Qovaryx FinanceDecoder — 12 decoder blocks, GQA, RoPE, SwiGLU FFN, RMSNorm, MTP heads, decision head.
• Recipe: Qovaryx crystallization discipline — train the law before replaying the noise.
• Runs on CPU. No GPU required at inference.

Architecture (Qovaryx proprietary)

• 53.5M parameters
• 12 decoder blocks, d_model=512, n_head=8, GQA n_kv_head=2
• SwiGLU FFN, RoPE positional, RMSNorm
• Multi-token prediction (MTP) auxiliary heads
• Decision head for routed-decision tasks
• Tokenizer: Qovaryx english_v1 BPE, vocab 32000 (in-house build)
• Pretrained from qovaryx-50m-scratch-base step 60000 — 491.5M tokens
• Full fine-tune (no LoRA, no QLoRA, no adapter): every parameter was updated on the Qovaryx crystal corpus for this specialist

How to load it (Python)

import torch
from tokenizers import Tokenizer
from bleeding_edge.model.decoder import FinanceDecoder, DecoderConfig

tok = Tokenizer.from_file("tokenizer.json")
ckpt = torch.load("pytorch_model.pt", map_location="cpu", weights_only=False)
cfg = DecoderConfig(**{k: v for k, v in ckpt["model_cfg"].items() if k in DecoderConfig.__dataclass_fields__})
cfg.vocab_size = tok.get_vocab_size()
model = FinanceDecoder(cfg).eval()
state = {k.removeprefix("_orig_mod."): v for k, v in ckpt["model_state"].items()}
model.load_state_dict(state, strict=False)

prompt = "Extract invoice total. JSON {total_value, currency}.\nINV-7777\nNet: 88.10\nVAT: 17.62\nTotal: 105.72 EUR"
ids = tok.encode(prompt).ids
cur = torch.tensor([ids], dtype=torch.long)
with torch.no_grad():
    for _ in range(120):
        nxt = int(torch.argmax(model(cur, return_decision=False).logits[:, -1, :], dim=-1))
        if nxt == 0: break
        cur = torch.cat([cur, torch.tensor([[nxt]])], dim=1)
print(tok.decode(cur[0].tolist()[len(ids):]))

License & posture

Apache 2.0 for the published weights, model card, and example code.

The Qovaryx scratch base build pipeline, the crystallization corpus, the eval gate constants, the cluster routing policy, and the protected runtime entrypoint are Qovaryx proprietary technology and are not included in this release. Same posture as every previous Qovaryx public release: ship the weights and the audit, not the recipe.

Sibling specialists in the Qovaryx Q-Office-Suite

All nine specialists share the qovaryx-50m-scratch-base and the same audit discipline. Use one directly; use all nine through the cluster shell.

• Q-Triage — ticket routing
• Q-DocCite — document citation
• Q-Invoice — invoice extraction
• Q-ToolCall — agent tool-calls
• Q-Meeting — meeting structuring
• Q-FinCite — 10-K/10-Q citation
• Q-CmdSafe — command safety triage
• Q-SheetExtract — spreadsheet extraction
• Q-Coder — Python code skeletons

Watermark

This release carries a SHA256 issue fingerprint inside release.json for tamper-detection and attribution.

Community & support

• Research devlog: https://github.com/thron-j/qovaryx-ai-research
• Discord (Qovaryx community): https://discord.gg/PtuHZDv5ju
• Ko-fi (we cover GPU bills): https://ko-fi.com/tjarvis91
• Qovaryx options decoder runtime: https://huggingface.co/Qovaryx/qovaryx-options-decoder-full-community

If you find a failure mode this card doesn't cover, open a discussion on this repo or come to the Discord — that's how the next crystal corpus gets written.