Instructions to use SZLHOLDINGS/SZL-AncientMath-0.8B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SZLHOLDINGS/SZL-AncientMath-0.8B-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SZLHOLDINGS/SZL-AncientMath-0.8B-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("SZLHOLDINGS/SZL-AncientMath-0.8B-v1")
model = AutoModelForMultimodalLM.from_pretrained("SZLHOLDINGS/SZL-AncientMath-0.8B-v1", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SZLHOLDINGS/SZL-AncientMath-0.8B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SZLHOLDINGS/SZL-AncientMath-0.8B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SZLHOLDINGS/SZL-AncientMath-0.8B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SZLHOLDINGS/SZL-AncientMath-0.8B-v1

SGLang

How to use SZLHOLDINGS/SZL-AncientMath-0.8B-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SZLHOLDINGS/SZL-AncientMath-0.8B-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SZLHOLDINGS/SZL-AncientMath-0.8B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SZLHOLDINGS/SZL-AncientMath-0.8B-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SZLHOLDINGS/SZL-AncientMath-0.8B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use SZLHOLDINGS/SZL-AncientMath-0.8B-v1 with Docker Model Runner:
```
docker model run hf.co/SZLHOLDINGS/SZL-AncientMath-0.8B-v1
```

SZL-AncientMath-0.8B-v1

A 0.8B fine-tune on seven ancient mathematical traditions — certified computation principles from Liu Hui, Madhava of Sangamagrama, Egyptian unit fractions, Babylonian sexagesimal, Greek constructive geometry, Andean khipu, and Indian zero — as a domain corpus for the estate's ancient-geometry research line.

Artifact: merged 16-bit checkpoint (not adapter-only) · Stage: research · Wave: ancientmath-w26 · Status: BASELINE_MEASURED_DELTA_PENDING

Why this model is different

Seven traditions, one corpus: each example pairs an ancient computation method with its modern formal equivalent and its honesty contract (what the ancient method did and did not establish)
Certified computation framing: examples emphasize methods that carried verifiable procedures (Liu Hui's exhaustion refinement, Madhava's series bounds), aligned with the estate's certified-computation doctrine
Cultural honesty: the corpus presents these traditions on their own terms with attribution; no claim of superiority in either direction

Model overview

Key	Value
Base model	Qwen 0.8B class (`szl-receiptagent-qwen35-0.8b-sft-v3`)
Parameters	0.8B, merged 16-bit
Training corpus	`SZLHOLDINGS/ancient-math-reasoning-sft-v1` — 24 examples
Method	QLoRA SFT (Unsloth + TRL), merged-16bit push
Hardware	NVIDIA RTX A6000 48GB (Massed Compute)
Wave log	`SZLHOLDINGS/wave-logs` (ancientmath-w26)

Training data

24 hand-curated examples across 7 traditions (Liu Hui, Madhava, Egyptian, Babylonian, Greek, Andean khipu, Indian zero), each pairing the ancient method with its modern formal equivalent and explicit statements of what the method did and did not prove.

Evaluation — measured, receipts published, failures verbatim

Measured ablation (PUBLISHED VERBATIM): in the three-way citation-probe ablation this variant measured 5/6 confabulations — the worst of the three (reasoning 4/6, free-energy 3/6). We publish that verbatim. 24 examples is a seed; the ablation shows it does not yet reduce confabulation.

Eval	Status	Result
Citation-probe ablation (6 probes)	MEASURED	5/6 confabulated — worst of three ablations, published verbatim

Governance

Model output is an untrusted proposal. Consequential actions pass a separate deterministic controller, policy gate (Λ-spine, fail-closed, zero-veto), and human approval boundary. The estate's measured confabulation floor at 0.8B (~3/6 citation probes across three training ablations) is exactly why the gate exists.

Deployment

vllm serve SZLHOLDINGS/SZL-AncientMath-0.8B-v1 --dtype bfloat16 --max-model-len 4096

No production endpoint serves this model. No x402 monetization route is bound to it.

Limitations (honest)

24 examples is a seed corpus — expect narrow transfer; no claim of general improvement
The only measured claim is the domain delta — everything else is unmeasured
0.8B scale has a measured confabulation floor; small models fabricate — gates exist for that reason
Not production-eligible; no autonomous execution

License & attribution

Apache-2.0 (per base model). Ancient sources treated with attribution and on their own terms; corpus authored in-house.

Citation

@misc{{szl-ancientmath-08b-v1,
  title  = {{SZL-AncientMath-0.8B-v1}} — SFT on ancient-math certified computation principles,
  author = {{Lutar, Stephen P. Jr.}},
  year   = {{2026}},
  url    = {{https://huggingface.co/SZLHOLDINGS/SZL-AncientMath-0.8B-v1}}
}}

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Safetensors

Model size

0.9B params

Tensor type

F32

BF16

Model tree for SZLHOLDINGS/SZL-AncientMath-0.8B-v1

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Adapter

SZLHOLDINGS/szl-receiptagent-qwen35-0.8b-v2

Finetuned

SZLHOLDINGS/szl-receiptagent-qwen35-0.8b-sft-v3

Finetuned

(16)

this model