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---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
- qlora
- governed-agent
- proposal-only
- receipt-verified
- szl-holdings
- alloy
---
<p align="center">
[![receipts](https://img.shields.io/badge/receipts-training%20+%20eval%20signed-3af4c8?style=flat-square)](https://huggingface.co/SZLHOLDINGS/SZL-Forge-1.5B-ReceiptAgent/blob/main/training_receipt.signed.json)
[![weights](https://img.shields.io/badge/weights-1.5B%20safetensors%20+%20LoRA-5b8dee?style=flat-square)](https://huggingface.co/SZLHOLDINGS/SZL-Forge-1.5B-ReceiptAgent/tree/main)
[![family](https://img.shields.io/badge/family-SZL--Forge-d7b96b?style=flat-square)](https://huggingface.co/SZLHOLDINGS)
</p>
<!--
Model card for SZL-Forge-1.5B-ReceiptAgent. Every number below is DERIVED from
the committed owner-signed receipts (training_receipt.signed.json +
eval_receipt.signed.json), which the Alloy backbone independently re-verifies
at /api/forge/family. Do NOT hand-edit a number here — regenerate it from the
receipts, or it becomes a fabrication. License Apache-2.0 matches the
Qwen2.5-1.5B-Instruct base; the operator may change it at any time.
-->
# SZL-Forge-1.5B-ReceiptAgent
A **governed, proposal-only** agent fine-tune of `Qwen/Qwen2.5-1.5B-Instruct`.
It **proposes** evidence-bound, approval-gated decision **drafts** as JSON — it
never finalizes, never executes, and never fabricates a number, citation, or
receipt. When asked to overstep that boundary, it **refuses**.
> **Provenance, not vibes.** Every capability claim on this card is backed by an
> ed25519 owner-signed receipt committed alongside the weights and
> **independently re-verified** by the Alloy backbone. Verify it yourself (see
> *Verify this model* below). Nothing here is asserted that a signature does not
> already prove.
## 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:** deterministic, schema-validated synthetic drafts + refusals;
every dataset file is sha256-pinned in the receipt and byte-reproducible.
## Evaluation (REPORTED — held-out, owner-signed)
Measured on a **held-out** curriculum, signed into `eval_receipt.signed.json`
and chained to the training receipt:
| Metric | Result |
| --- | --- |
| Draft-conformance (schema-valid drafts) | **5 / 5 (100%)** |
| Adversarial-refusal (correctly refused overstep) | **6 / 6 (100%)** |
| Sanity gate (train-set reproduction, pre-eval) | drafts 15/15 · refusals 8/8 |
The adversarial-refusal rate — not the memorizable conformance rate — is the
meaningful honesty score.
## Verify this model (don't trust — check)
1. The two receipts are ed25519-signed over a canonical JSON string and
**hash-chained** (`eval.trainingReceiptSha256` == `sha256(trainingCanonical)`).
2. The signing key is committed as `owner_pubkey.json`
(`keyId e7f01810aaa97394`); its `keyId` re-derives from the SPKI.
3. Every `datasets[*]` sha256 in the receipts equals the committed curriculum
files, and the output-schema sha equals `receiptagent.schema.json`.
4. The Alloy backbone re-runs all of the above per request and exposes the
verdict at **`/api/forge/family`** (`evidence.trainingStatus` /
`evidence.evalStatus`). Anyone can reproduce it against these files.
**Honesty stance:** results are `REPORTED` (produced on owner metal), not
`MEASURED` by a third party. Trust anchor is `REPO_DECLARED` (the key ships in
this repo); it upgrades to `PINNED` when the operator pins `keyId` out-of-band.
## 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` the same way.
- `owner_pubkey.json`, `training_receipt.signed.json`, `eval_receipt.signed.json`,
`receiptagent.schema.json` — the verifiable provenance bundle.
- Any `*.gguf` is a **derived** convenience for llama.cpp / Ollama and is **not**
covered by the signed weights hash.
## 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.
---
<p align="center">
<a href="https://huggingface.co/SZLHOLDINGS">SZL Holdings</a> ·
<a href="https://a-11-oy.com">a-11-oy.com</a> ·
<a href="https://github.com/szl-holdings/szl-forge">szl-forge (GitHub source · forge kit)</a> ·
<a href="https://huggingface.co/SZLHOLDINGS/SZL-Khipu-1.5B-GGUF">Khipu GGUF</a>
</p>
<p align="center"><sub>SLSA: L1 honest · L2 attested · L3 roadmap. Λ = Conjecture 1. Trust ceiling 0.97.</sub></p>

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{
"canonical_flagship": "https://github.com/szl-holdings/a11oy",
"canonical_github_org": "https://github.com/szl-holdings",
"canonical_rule": "GitHub source, protected CI, release receipts, checksums, and provenance are authoritative. Hugging Face is the managed Hub surface.",
"clone_of": null,
"generation": "3c20fe2a6b0c9c56a045265b194a81e952fc949c",
"managed_at": "2026-07-22T08:48:33.215036+00:00",
"non_destructive": true,
"observed_sha_before_upgrade": "67043d13c3166a81cfd292c84b18e587781f3ba9",
"organization": "SZLHOLDINGS",
"repo_id": "SZLHOLDINGS/SZL-Forge-1.5B-ReceiptAgent",
"repo_type": "model",
"runtime": null,
"schema": "szl.hf-estate-managed/v1"
}

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---
base_model: unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## Glossary [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.19.1

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{
"alora_invocation_tokens": null,
"alpha_pattern": {},
"arrow_config": null,
"auto_mapping": {
"base_model_class": "Qwen2ForCausalLM",
"parent_library": "transformers.models.qwen2.modeling_qwen2",
"unsloth_fixed": true
},
"base_model_name_or_path": "unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit",
"bias": "none",
"corda_config": null,
"ensure_weight_tying": false,
"eva_config": null,
"exclude_modules": null,
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 64,
"lora_bias": false,
"lora_dropout": 0,
"lora_ga_config": null,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": null,
"peft_type": "LORA",
"peft_version": "0.19.1",
"qalora_group_size": 16,
"r": 32,
"rank_pattern": {},
"revision": null,
"target_modules": [
"gate_proj",
"q_proj",
"k_proj",
"down_proj",
"v_proj",
"o_proj",
"up_proj"
],
"target_parameters": null,
"task_type": "CAUSAL_LM",
"trainable_token_indices": null,
"use_bdlora": null,
"use_dora": false,
"use_qalora": false,
"use_rslora": false
}

3
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version https://git-lfs.github.com/spec/v1
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size 147770496

54
chat_template.jinja Normal file
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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

62
config.json Normal file
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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": null,
"torch_dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 1536,
"initializer_range": 0.02,
"intermediate_size": 8960,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 21,
"model_type": "qwen2",
"num_attention_heads": 12,
"num_hidden_layers": 28,
"num_key_value_heads": 2,
"pad_token_id": 151654,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"unsloth_fixed": true,
"unsloth_version": "2026.7.2",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

31
eval_receipt.signed.json Normal file
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{
"payload": {
"kind": "szl-forge-eval-receipt",
"v": 1,
"capabilityProfile": "SZL-Forge-1.5B-ReceiptAgent",
"baseModel": "Qwen/Qwen2.5-1.5B-Instruct",
"datasets": {
"train.jsonl": "775e25b526a96d1486e80aae048f731bdad02a0d94ea76144593e115802fa24f",
"eval.jsonl": "1a92bea9b163882ae7c7aa00be6c1fda3e9c98683f0214af985bfb184e27ff65",
"train.refusals.jsonl": "c5136b612951c209d1041839d1ac19a8fa378c31015d61dd7565994b4f3e2b47",
"adversarial.jsonl": "81d2a494ee41256c7909a54435c4e7971fea65de81fffbf3f2b8a008aab5a572",
"receiptagent.schema.json": "1db07ef9e1581cc8cc471dd42e79a21b728c4e3bf1222fd0f06f2b7257bb288e"
},
"schemaFingerprintSha256": "68b41b9f198bef4238e561f66456a849afe8095a43df19299a434915b54361d5",
"outputSchemaSha256": "1db07ef9e1581cc8cc471dd42e79a21b728c4e3bf1222fd0f06f2b7257bb288e",
"weightsArtifactSha256": "851af12990cc2077bb610ebe3e4511a077f1fc59f8e7ebe21d40c73a7da64c69",
"servedModel": "receiptagent",
"trainingReceiptSha256": "9f342f33f295888f95490359bc87c2425ce80866dce8d4f1aa38ed5dcb54d93e",
"evalTotal": 5,
"evalContractValid": 5,
"adversarialTotal": 6,
"adversarialRefused": 6,
"evaluatedAt": "2026-07-13T21:36:22.878211+00:00",
"host": "betterwithage",
"keyId": "e7f01810aaa97394"
},
"canonical": "{\"adversarialRefused\":6,\"adversarialTotal\":6,\"baseModel\":\"Qwen/Qwen2.5-1.5B-Instruct\",\"capabilityProfile\":\"SZL-Forge-1.5B-ReceiptAgent\",\"datasets\":{\"adversarial.jsonl\":\"81d2a494ee41256c7909a54435c4e7971fea65de81fffbf3f2b8a008aab5a572\",\"eval.jsonl\":\"1a92bea9b163882ae7c7aa00be6c1fda3e9c98683f0214af985bfb184e27ff65\",\"receiptagent.schema.json\":\"1db07ef9e1581cc8cc471dd42e79a21b728c4e3bf1222fd0f06f2b7257bb288e\",\"train.jsonl\":\"775e25b526a96d1486e80aae048f731bdad02a0d94ea76144593e115802fa24f\",\"train.refusals.jsonl\":\"c5136b612951c209d1041839d1ac19a8fa378c31015d61dd7565994b4f3e2b47\"},\"evalContractValid\":5,\"evalTotal\":5,\"evaluatedAt\":\"2026-07-13T21:36:22.878211+00:00\",\"host\":\"betterwithage\",\"keyId\":\"e7f01810aaa97394\",\"kind\":\"szl-forge-eval-receipt\",\"outputSchemaSha256\":\"1db07ef9e1581cc8cc471dd42e79a21b728c4e3bf1222fd0f06f2b7257bb288e\",\"schemaFingerprintSha256\":\"68b41b9f198bef4238e561f66456a849afe8095a43df19299a434915b54361d5\",\"servedModel\":\"receiptagent\",\"trainingReceiptSha256\":\"9f342f33f295888f95490359bc87c2425ce80866dce8d4f1aa38ed5dcb54d93e\",\"v\":1,\"weightsArtifactSha256\":\"851af12990cc2077bb610ebe3e4511a077f1fc59f8e7ebe21d40c73a7da64c69\"}",
"signatureBase64": "8YWgeNs/xy3IL12PKMhJpe1V09AD68U4Hsk/kq2rjNUfvlUCbuEaUwExlvZiG7OO5CCfEYFk8S8ZpHFI8BXQAQ==",
"publicKeySpkiBase64": "MCowBQYDK2VwAyEA0hrzlPL52zi3yA9Wj1uKL1CJMr12dTwKeIXYfJCg3a8=",
"keyId": "e7f01810aaa97394"
}

14
generation_config.json Normal file
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{
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"max_length": 32768,
"pad_token_id": 151654,
"repetition_penalty": 1.1,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.5.0"
}

3
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5
owner_pubkey.json Normal file
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{
"algo": "ed25519",
"publicKeySpkiBase64": "MCowBQYDK2VwAyEA0hrzlPL52zi3yA9Wj1uKL1CJMr12dTwKeIXYfJCg3a8=",
"keyId": "e7f01810aaa97394"
}

121
receiptagent.schema.json Normal file
View File

@@ -0,0 +1,121 @@
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"proposalId": {
"description": "Opaque draft id. In this example it is obviously synthetic.",
"type": "string"
},
"capabilityProfile": {
"description": "The governed capability profile contracted to emit this draft.",
"type": "string",
"const": "SZL-Forge-1.5B-ReceiptAgent"
},
"provenance": {
"description": "Honest origin of the draft: SYNTHETIC = an illustrative example not produced by any model (as in RECEIPT_AGENT_EXAMPLE below); MODEL_PROPOSED = a real draft proposed by the ReceiptAgent model. Both are honest; a draft can never claim any other origin (e.g. HUMAN or MEASURED).",
"type": "string",
"enum": [
"SYNTHETIC",
"MODEL_PROPOSED"
]
},
"claim": {
"description": "The proposed decision, stated as a DRAFT for human review.",
"type": "string"
},
"decision": {
"description": "Always a draft — the model proposes; it never decides or finalizes.",
"type": "string",
"const": "DRAFT"
},
"evidence": {
"description": "Evidence-bound: at least one cited source backs the claim.",
"minItems": 1,
"type": "array",
"items": {
"type": "object",
"properties": {
"sourceEndpoint": {
"description": "A real Alloy endpoint the controller would consult for this evidence (or a plainly-marked placeholder) — no live value is embedded here.",
"type": "string"
},
"label": {
"description": "The source endpoint's OWN honesty tier — denotes what kind of evidence it provides, not a measurement captured in this synthetic example.",
"type": "string",
"enum": [
"MEASURED",
"REPORTED",
"DECLARED",
"SIMULATED",
"UNKNOWN",
"UNAVAILABLE"
]
},
"note": {
"description": "Plain-language description of the evidence role.",
"type": "string"
}
},
"required": [
"sourceEndpoint",
"label",
"note"
],
"additionalProperties": false
}
},
"approvalRequired": {
"description": "Approval-gated: the draft can never proceed without human approval.",
"type": "boolean",
"const": true
},
"controllerBoundary": {
"description": "States the A11oy runtime boundary — the controller decides, validates, gates on approval, and executes OUTSIDE the weights.",
"type": "string"
},
"receiptBinding": {
"description": "How an approved, executed action would bind to a signed receipt.",
"type": "object",
"properties": {
"algorithm": {
"description": "The estate's real receipt scheme this draft would bind to on execution.",
"type": "string",
"const": "ed25519-over-sha256"
},
"status": {
"description": "A synthetic, unexecuted draft is NOT receipt-bound; a real receipt is minted only when an approved action actually executes.",
"type": "string",
"const": "NOT_BOUND"
},
"note": {
"description": "Why no receipt exists for this example.",
"type": "string"
}
},
"required": [
"algorithm",
"status",
"note"
],
"additionalProperties": false
},
"executed": {
"description": "A draft never self-executes; execution happens outside the model.",
"type": "boolean",
"const": false
}
},
"required": [
"proposalId",
"capabilityProfile",
"provenance",
"claim",
"decision",
"evidence",
"approvalRequired",
"controllerBoundary",
"receiptBinding",
"executed"
],
"additionalProperties": false
}

3
tokenizer.json Normal file
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194
tokenizer_config.json Normal file
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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"model_max_length": 32768,
"pad_token": "<|vision_pad|>",
"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null,
"added_tokens_decoder": {
"151643": {
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"single_word": false,
"lstrip": false,
"rstrip": false,
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},
"151646": {
"content": "<|object_ref_start|>",
"single_word": false,
"lstrip": false,
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"special": true
},
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"single_word": false,
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"special": true
},
"151648": {
"content": "<|box_start|>",
"single_word": false,
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"special": true
},
"151649": {
"content": "<|box_end|>",
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"lstrip": false,
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"special": true
},
"151650": {
"content": "<|quad_start|>",
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"lstrip": false,
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"special": true
},
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"single_word": false,
"lstrip": false,
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},
"151652": {
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},
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"special": true
},
"151655": {
"content": "<|image_pad|>",
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"rstrip": false,
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"special": true
},
"151656": {
"content": "<|video_pad|>",
"single_word": false,
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},
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"content": "<tool_call>",
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},
"151658": {
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"single_word": false,
"lstrip": false,
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"special": false
},
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"content": "<|fim_prefix|>",
"single_word": false,
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"normalized": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
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},
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"content": "<|repo_name|>",
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"special": false
},
"151664": {
"content": "<|file_sep|>",
"single_word": false,
"lstrip": false,
"rstrip": false,
"normalized": false,
"special": false
}
},
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
}

28
training_receipt.signed.json Normal file
View File

@@ -0,0 +1,28 @@
{
"payload": {
"kind": "szl-forge-training-receipt",
"v": 1,
"capabilityProfile": "SZL-Forge-1.5B-ReceiptAgent",
"baseModel": "Qwen/Qwen2.5-1.5B-Instruct",
"datasets": {
"train.jsonl": "775e25b526a96d1486e80aae048f731bdad02a0d94ea76144593e115802fa24f",
"eval.jsonl": "1a92bea9b163882ae7c7aa00be6c1fda3e9c98683f0214af985bfb184e27ff65",
"train.refusals.jsonl": "c5136b612951c209d1041839d1ac19a8fa378c31015d61dd7565994b4f3e2b47",
"adversarial.jsonl": "81d2a494ee41256c7909a54435c4e7971fea65de81fffbf3f2b8a008aab5a572",
"receiptagent.schema.json": "1db07ef9e1581cc8cc471dd42e79a21b728c4e3bf1222fd0f06f2b7257bb288e"
},
"schemaFingerprintSha256": "68b41b9f198bef4238e561f66456a849afe8095a43df19299a434915b54361d5",
"outputSchemaSha256": "1db07ef9e1581cc8cc471dd42e79a21b728c4e3bf1222fd0f06f2b7257bb288e",
"adapterSha256": "5eb426c1a4cd17faa4c46590d3aff22671023f64a179e7b47b5bcab4c59e47cd",
"weightsArtifactSha256": "851af12990cc2077bb610ebe3e4511a077f1fc59f8e7ebe21d40c73a7da64c69",
"servedModel": "receiptagent",
"trainedAt": "2026-07-13T21:33:44.531784+00:00",
"host": "betterwithage",
"finalTrainLoss": "0.1038",
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