SZLHOLDINGS

SZL-Forge-1.5B-ReceiptAgent

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README

License: apache-2.0

What it does

Emits a single JSON draft conforming to the ReceiptAgent output schema: decision=DRAFT, approvalRequired=true, executed=false, provenance=MODEL_PROPOSED, receiptBinding.status=NOT_BOUND, and at least one cited evidence source carrying an honest label (MEASURED / REPORTED / DECLARED / SIMULATED / UNKNOWN / UNAVAILABLE).
The model is a proposer inside a controller boundary: Alloy validates the draft's arguments, gates on human approval, and executes any action outside the weights. The model never decides or acts.

Training (REPORTED — owner-metal, not server-measured)

Base model: Qwen/Qwen2.5-1.5B-Instruct
Method: QLoRA SFT with response-only loss masking and refusal oversampling (the adversarial refusal set is held out from training). Full, reproducible recipe: train_receiptagent.py in the forge kit.
Final train loss: 0.1038 (REPORTED by the owner's signed training receipt)
Trained at: 2026-07-13T21:33:44Z on host betterwithage
Curriculum: synthetic drafts and refusals. Their hashes are recorded in the signed receipt, but the curriculum files are not published in this model repo; the training data cannot be independently reconstructed from this repository alone.

Evaluation (OWNER-RUN, REPORTED)

The committed evaluation receipt records a small held-out synthetic harness:

Table with columns: Metric, Receipt-bound result
Metric	Receipt-bound result
Draft conformance	5 / 5
Adversarial refusal	6 / 6

These owner-run results are not a third-party benchmark. The sample is too small to justify an autonomous, high-stakes, or universal-refusal claim; keep a validating controller and fail closed.

Verify this model (do not trust - check)

Verify both Ed25519 signatures over each receipt's canonical JSON.
Re-derive keyId as the first 16 hex characters of SHA-256 over the SPKI bytes.
Recompute the evaluation-to-training chain from the training canonical JSON.
Recompute the committed receiptagent.schema.json hash and compare it with the receipt.
Treat the curriculum hashes as owner assertions here: their source files are not present in this model repository, so they cannot be independently recomputed here.

Evidence label: REPORTED, owner-run. Trust anchor: REPO_DECLARED. No third-party benchmark, external key pin, or production deployment is claimed.

Files & provenance bindings

Merged model weights (*.safetensors) — the receipts' weightsArtifactSha256 is a deterministic digest over the sorted *.safetensors of the merge (basename + bytes), reproducible with sha256_safetensors_dir in the forge kit. This — not any GGUF — is the artifact the signed weights hash covers.
LoRA adapter (*.safetensors) - bound by adapterSha256 using the forge-kit digest method.
Uploaded-byte hashes (Hub LFS OIDs): model.safetensors = a797592fb35c32cc6ebf5739c0a1d9312123300acaf09987d207d86196bc6e7d; adapter/adapter_model.safetensors = 706aff139489d62cb1ac221c0f92a1a8595cd6a42032c542efe8028684e57cf2. These bind the exact Hub uploads and are distinct from the receipt's forge-kit digests.
owner_pubkey.json, , , — the verifiable provenance bundle.

Run it

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SZLHOLDINGS/SZL-Forge-1.5B-ReceiptAgent"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Draft a decision on raising the rolling-24h spend cap."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512, do_sample=False)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

The model returns a single JSON draft (decision=DRAFT, approvalRequired=true, executed=false) or a refusal — never a finalized action. Validate the output against receiptagent.schema.json before acting on it.

Adapter (PEFT) alternative

The LoRA adapter ships under adapter/ for stacking on the stock base:

python
from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-1.5B-Instruct", torch_dtype="auto", device_map="auto"
)
model = PeftModel.from_pretrained(
    base, "SZLHOLDINGS/SZL-Forge-1.5B-ReceiptAgent", subfolder="adapter"
)

Intended use & limits

Use: proposing governed, evidence-cited decision drafts for a human-in-the-loop controller (e.g. Alloy).
Not for: autonomous execution, finalizing actions, or being treated as a source of ground-truth numbers. It is a 1.5B proposer, not an oracle.

Citation

Part of the SZL-Forge family by SZL Holdings. Provenance verifiable via the Alloy governed-inference backbone.

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Model Details

Model Provider

SZLHOLDINGS

Model Tree

Base

Qwen/Qwen2.5-1.5B-Instruct

Fine-tuned

this model

Input Modalities

Text

Output Modalities