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

Libraries

How to use SZLHOLDINGS/SZL-Showcase-Systems-v1 with Transformers:

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

pipe = pipeline("text-generation", model="SZLHOLDINGS/SZL-Showcase-Systems-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-Showcase-Systems-v1")
model = AutoModelForMultimodalLM.from_pretrained("SZLHOLDINGS/SZL-Showcase-Systems-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-Showcase-Systems-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SZLHOLDINGS/SZL-Showcase-Systems-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-Showcase-Systems-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SZLHOLDINGS/SZL-Showcase-Systems-v1

SGLang

How to use SZLHOLDINGS/SZL-Showcase-Systems-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-Showcase-Systems-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-Showcase-Systems-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-Showcase-Systems-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-Showcase-Systems-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

SZL-Showcase-Systems-v1

SZL Holdings estate-tuned model — high-value showcase systems wave (w23). Fine-tuned on a corpus built from the 20 showcase repositories of the szl-holdings organization.

Training (MEASURED)

Base: SZLHOLDINGS/szl-receiptagent-qwen35-0.8b-sft-v3
Dataset: SZLHOLDINGS/high-value-systems-corpus-v1 (2,728 examples from 20 showcase repos)
Method: QLoRA SFT via Unsloth + TRL on NVIDIA A6000 (Massed Compute), merged 16-bit push
Wave: szl-high-value-showcase-w23 — completed, WAVE_EXITED_RC=0 (see SZLHOLDINGS/wave-logs)
Trained by: SZL Holdings (betterwithage)

Evaluation status: NOT_RUN

No held-out evaluation or benchmark scores exist for this model yet. No capability claims are made. Treat outputs as unvalidated until eval receipts are published to SZLHOLDINGS/model-eval-receipts.

Limitations

Corpus is source-derived documentation/code text; no held-out split scores yet
No abstention counterweight training in this wave
No hallucination/confabulation measurement yet
Not published as production-eligible; no x402 endpoint serves this model

Honest-use doctrine

Per SZL doctrine: model output is an untrusted proposal. Any consequential action must pass a separate deterministic controller and human approval boundary.

Downloads last month: 39

Safetensors

Model size

0.9B params

Tensor type

F32

BF16

Model tree for SZLHOLDINGS/SZL-Showcase-Systems-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

(8)

this model