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
```

matthh commited on Jul 13, 2024

Commit

e67d2ae

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1 Parent(s): 00d23c0

Update README.md

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@@ -2,4 +2,19 @@
 license: mit
 language:
 - en
----

 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