Instructions to use ATinyGreenCell/scadcoder-qwen3.5-9b-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ATinyGreenCell/scadcoder-qwen3.5-9b-v3 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-v3")
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-v3")
model = AutoModelForMultimodalLM.from_pretrained("ATinyGreenCell/scadcoder-qwen3.5-9b-v3", 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-v3 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-v3"
# 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-v3",
		"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-v3

SGLang

How to use ATinyGreenCell/scadcoder-qwen3.5-9b-v3 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-v3" \
    --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-v3",
		"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-v3" \
        --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-v3",
		"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-v3 with Docker Model Runner:
```
docker model run hf.co/ATinyGreenCell/scadcoder-qwen3.5-9b-v3
```

scadcoder-qwen3.5-9b-v3

A 3-epoch LoRA fine-tune of Qwen3.5-9B for parametric bio-lab OpenSCAD, for the Tiiny Pocket Lab (v3: v2's data plus Opus-distilled and rejection-sampled examples).

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

8,784 single-turn examples, 3 epochs, LoRA rank 32 (dropout 0.1) on the language layers:

the 7,238 render-verified examples of v2 (scadcoder-qwen3.5-9b-3ep): 34 parametric part families plus repair examples;
336 examples distilled from Claude Opus on bench-style part descriptions, each render-verified (watertight, bounding box within 15% of the specification), weighted ×3;
269 of the model's own rejection-sampled generations that passed the verifier, weighted ×2.

Evaluation caveat. Several of the distilled part families follow the wording of Prompt-to-Bench task prompts (e.g. tube racks, funnels, gel combs, adapter sleeves) with different dimensions, so this model's scores on those tasks are in-family, not held-out transfer. v2 is the held-out baseline. Details: https://github.com/ATinyGreenCell/prompt-to-bench (§6.4).

Downloads last month: 21

Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for ATinyGreenCell/scadcoder-qwen3.5-9b-v3

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