Instructions to use SZLHOLDINGS/chaski with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SZLHOLDINGS/chaski with Transformers:

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

pipe = pipeline("text-generation", model="SZLHOLDINGS/chaski")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("SZLHOLDINGS/chaski")
model = AutoModelForMultimodalLM.from_pretrained("SZLHOLDINGS/chaski", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SZLHOLDINGS/chaski with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SZLHOLDINGS/chaski"
# 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/chaski",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SZLHOLDINGS/chaski

SGLang

How to use SZLHOLDINGS/chaski 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/chaski" \
    --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/chaski",
		"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/chaski" \
        --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/chaski",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SZLHOLDINGS/chaski with Docker Model Runner:
```
docker model run hf.co/SZLHOLDINGS/chaski
```

Chaski

KANCHAY · Doctrine v11 · Lean 749/14/163 · Λ = Conjecture 1 (advisory) · a-11-oy.com

Adapters are on this repo. Named-N evals MEASURED json_draft 0/5, adversarial_refusal 2/6, not a pass. Not publication-eligible.

One line. Messenger LLM. Proposal-only drafts and honest refusals for the SZL controller.

The cut

Multimodal models narrate what they see. Chaski may only carry a payload the controller already signed. Vision without authorship.

A courier that cannot invent the dispatch. The oldest job in the Andes, as a LoRA.

Silhouette → leave → SZL

Leader	Take, then tweak
Anthropic	Claude vision, minus the right to conclude.
NVIDIA	NVLM / NeMo multimodal, minus the right to act.
Unsloth	Adapter on Qwen3.5-0.8B.

Nobody else ships this combination. That is the point of a one-of-one.

Intended use

Carry signed payloads across organs.

Limitations

proposal-only
Do not treat as a VLM product card without a signed vision eval.

Canonical GitHub: szl-holdings/szl-forge


Artifact	`adapter_model.safetensors` + merged ~1.7GB shard AVAILABLE
Parent / eval revision	`1c55df8652e9d0f7b84356b1e2d54849165ae884`
Originality	SZL fine-tune of a disclosed Apache Qwen instruct base
Base	`Qwen/Qwen3.5-0.8B` (Apache-2.0, 0.6B–2B lock). Not an Unsloth-default card.
License	`apache-2.0`
HF Jobs	Attempt 5 COMPLETED `6a91bf1045686a1580c12105` (`report_to=none`). Tensors on Hub.
Named-N	MEASURED fail. `json_draft` 0/5. `adversarial_refusal` 2/6. Not a pass.
Receipt	`eval_report.json` 3996 bytes sha256 `4d057eb9867285e69b00222be110bbb660330a96fe7b284a4d7f488268a13e05` (commit `db71c24`)
Method	in-process greedy generate, `messages[:-1]`, transformers 5.16.1 bf16 CPU, `load_in_4bit=False`. Date 2026-08-28. File `eval_report.json`. PR 63.
Quality	ROADMAP (prose). Failed gate is not a publish.
Status	CUTTING
Lab	House CPU lab stays Khipu GGUF. Lab load forbidden for Chaski.
Later SKU	`A11OY-MINI` GGUFs exist on that repo. They do not inherit this Named-N. Mini stays evals none-this-run.
Sibling	`szl-receiptagent-qwen35-0.8b-v2`

Fashion rule. Silhouette from Qwen3 / Qwen3.5 instruct. Cut is original SZL. We do not republish someone else's tensors.

Intended use

Who: a11oy / Alloy controller, not an end-user chatbot
What: JSON drafts (decision=DRAFT, approvalRequired=true, executed=false) and doctrine-faithful UNKNOWN
Where: behind a validating controller. The weights propose. The controller gates.

What it is NOT

Not an autonomous agent, executor, factual oracle, or weapon.
Not a Qwen rehost.
Not the live lab model. Not a tokens/s claim.
Not publication-eligible on this MEASURED run.
Not a 5/5 or 6/6. Do not read 2/6 as a pass.

Evaluation

Named-N MEASURED fail on live Chaski 1c55df8 (2026-08-28). Receipt: eval_report.json sha256 4d057eb9867285e69b00222be110bbb660330a96fe7b284a4d7f488268a13e05.

Probe	N	Score	Label
`json_draft`	5	0/5	MEASURED fail
`adversarial_refusal`	6	2/6	MEASURED fail

Method: in-process greedy generate, messages[:-1], transformers 5.16.1 bf16 CPU, load_in_4bit=False on live Chaski 1c55df8 (2026-08-28). File: eval_report.json. PR 63. What this is NOT: a passing eval gate, a published score, an A11OY-MINI eval, or a 5/5. publication_eligible: false.

train_loss MEASURED 1.783925924450159 is a train metric, not an eval.

Training

Recipe: Unsloth QLoRA SFT. Script: train_chaski.py. Loads only szl_dataset.jsonl.
1–2 FAILED: 6a91b8ba984507d9db4ea071 CastError; 6a91b990984507d9db4ea077 pyyaml 30s timeout.
3 COMPLETED receipt-only: 6a91ba00984507d9db4ea07f.
4 ERROR, not RUNNING: 6a91bb7c984507d9db4ea0a4 Trackio 404. Files persisted.
5 COMPLETED report_to=none: 6a91bf1045686a1580c12105. Tensors now on Hub.
Trackio: 404 betterwithage/trackio-bucket. No dashboard URL.
publication_eligible: false