Instructions to use matthh/git-image-to-g-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matthh/git-image-to-g-code with Transformers:

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

pipe = pipeline("image-text-to-text", model="matthh/git-image-to-g-code")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("matthh/git-image-to-g-code")
model = AutoModelForImageTextToText.from_pretrained("matthh/git-image-to-g-code")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use matthh/git-image-to-g-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matthh/git-image-to-g-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matthh/git-image-to-g-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/matthh/git-image-to-g-code

SGLang

How to use matthh/git-image-to-g-code 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 "matthh/git-image-to-g-code" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matthh/git-image-to-g-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "matthh/git-image-to-g-code" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matthh/git-image-to-g-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use matthh/git-image-to-g-code with Docker Model Runner:
```
docker model run hf.co/matthh/git-image-to-g-code
```

git-image-to-g-code / README.md

matthh

Update README.md

e67d2ae verified almost 2 years ago

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	---
	license: mit
	language:
	- en
	---

	# learning_to_draw_02

	## G-Code Generation with AI

	G-code instructs 3D printers and 2D plotters using simple "move to" commands with X and Y coordinates in 2D or 3D space.

	Inspired by my interest in machine drawing, this project uses the latest open-source AI models to create a Large Language Model (LLM) that generates G-code from images.

	The dataset, generated procedurally, includes both images and corresponding G-code. I developed the Python code for this project with the help of ChatGPT for quick suggestions and Claude.ai for debugging and refinement.

	This project demonstrates the innovative use of AI in automating G-code generation for creative and practical applications.


	### Transformer based gcode instruction generator