Instructions to use ATinyGreenCell/scadcoder-qwen3.5-9b-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ATinyGreenCell/scadcoder-qwen3.5-9b-v5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="ATinyGreenCell/scadcoder-qwen3.5-9b-v5")
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("ATinyGreenCell/scadcoder-qwen3.5-9b-v5")
model = AutoModelForMultimodalLM.from_pretrained("ATinyGreenCell/scadcoder-qwen3.5-9b-v5", 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 ATinyGreenCell/scadcoder-qwen3.5-9b-v5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ATinyGreenCell/scadcoder-qwen3.5-9b-v5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ATinyGreenCell/scadcoder-qwen3.5-9b-v5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ATinyGreenCell/scadcoder-qwen3.5-9b-v5

SGLang

How to use ATinyGreenCell/scadcoder-qwen3.5-9b-v5 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 "ATinyGreenCell/scadcoder-qwen3.5-9b-v5" \
    --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": "ATinyGreenCell/scadcoder-qwen3.5-9b-v5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "ATinyGreenCell/scadcoder-qwen3.5-9b-v5" \
        --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": "ATinyGreenCell/scadcoder-qwen3.5-9b-v5",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ATinyGreenCell/scadcoder-qwen3.5-9b-v5 with Docker Model Runner:
```
docker model run hf.co/ATinyGreenCell/scadcoder-qwen3.5-9b-v5
```

scadcoder-qwen3.5-9b-v5

A LoRA fine-tune of Qwen3.5-9B for parametric bio-lab OpenSCAD, for the Tiiny Pocket Lab (v5).

Base: Qwen/Qwen3.5-9B (arch Qwen3_5ForConditionalGeneration); vision encoder preserved, language layers LoRA-merged to bf16 safetensors.
Run with reasoning/thinking off.

Training data

v5 = v4 plus 1,500 multi-turn repair conversations in the benchmark's own shape: a part description, a flawed OpenSCAD file (one top-of-file parameter perturbed), the benchmark's measurement report of that flawed part, and the correct file. The flawed turn is masked from the loss (it is context only). 10,636 rows, 3 epochs, LoRA rank 32. v4's data: the 7,238 render-verified examples of v2 + 269 rejection-sampled generations (x2) + 680 Claude Opus distilled examples on 17 part families that are not Prompt-to-Bench tasks (x2). Details: https://github.com/ATinyGreenCell/prompt-to-bench (§6.4).

Downloads last month: 32

Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for ATinyGreenCell/scadcoder-qwen3.5-9b-v5

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