Robotics
Transformers
Safetensors
qwen2
text-generation
cnc
gcode
spatial-reasoning
qwen
text-generation-inference
Instructions to use vanishingradient/Instruct2GCode-Qwen2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vanishingradient/Instruct2GCode-Qwen2.5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("vanishingradient/Instruct2GCode-Qwen2.5") model = AutoModelForCausalLM.from_pretrained("vanishingradient/Instruct2GCode-Qwen2.5") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -44,6 +44,8 @@ inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.1, do_sample=False)
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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<SPATIAL_REASONING>
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Bounds: X[25 to 175], Y[25 to 105]
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Key Waypoints: [[25, 25], [175, 25], [175, 105], [25, 105], [25, 25]]
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...
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M30 ; End program
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```
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## ⚠️ Limitations & Safety
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- **Experimental Only:** This model generates physical toolpaths. If executed on a real CNC machine without simulation routing, it **may crash your spindle** or cause hardware damage.
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outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.1, do_sample=False)
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print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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```
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<SPATIAL_REASONING>
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Bounds: X[25 to 175], Y[25 to 105]
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Key Waypoints: [[25, 25], [175, 25], [175, 105], [25, 105], [25, 25]]
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...
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M30 ; End program
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```
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```
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## ⚠️ Limitations & Safety
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- **Experimental Only:** This model generates physical toolpaths. If executed on a real CNC machine without simulation routing, it **may crash your spindle** or cause hardware damage.
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