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
| base_model: Qwen/Qwen2.5-0.5B | |
| library_name: transformers | |
| tags: | |
| - robotics | |
| - cnc | |
| - gcode | |
| - spatial-reasoning | |
| - qwen | |
| datasets: | |
| - vanishingradient/Instruct2GCode-Synthetic-Dataset | |
| license: mit | |
| # 🤖 Instruct2GCode: Spatial Chain-of-Thought for CNC Machining | |
| ## 📝 Model Overview | |
| **Instruct2GCode** is an experimental Small Language Model (SLM) fine-tuned to translate natural language instructions directly into deterministic, mathematically precise CNC milling G-Code. | |
| Traditional LLMs hallucinate continuous physical dimensions. This model utilizes **Spatial Chain-of-Thought (S-CoT)** anchoring—forcing the attention mechanism to first predict a geometric bounding box and Cartesian waypoints before emitting the actual G-Code sequence. | |
| - **Base Model:** `Qwen/Qwen2.5-0.5B` | |
| - **Training Paradigm:** 4-bit QLoRA using synthetic S-CoT pairs | |
| - **Language:** English / G-Code (RS-274) | |
| - **License:** MIT | |
| ## 💻 How to Use | |
| To utilize the Spatial-CoT mechanism, you must allow the model to generate the intermediate `<SPATIAL_REASONING>` block prior to printing the code. | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| import torch | |
| model_id = "vanishingradient/Instruct2GCode-Qwen2.5" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") | |
| prompt = "Write CNC G-code to mill a rectangle with width 150mm and height 80mm starting at X=25, Y=25. Use a feed rate of 350 mm/min." | |
| # Instruct2GCode is trained on ChatML conversation formats | |
| text = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n" | |
| inputs = tokenizer(text, return_tensors="pt").to(model.device) | |
| outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.1, do_sample=False) | |
| print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)) | |
| ``` | |
| ``` | |
| <SPATIAL_REASONING> | |
| Bounds: X[25 to 175], Y[25 to 105] | |
| Key Waypoints: [[25, 25], [175, 25], [175, 105], [25, 105], [25, 25]] | |
| </SPATIAL_REASONING> | |
| ```gcode | |
| G21 ; Set units to metric | |
| G90 ; Absolute positioning | |
| G0 Z5 ; Safe Z height | |
| G0 X25 Y25 ; Move to start | |
| ... | |
| M30 ; End program | |
| ``` | |
| ## ⚠️ Limitations & Safety | |
| - **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. | |
| - **Domain Constraint:** Currently restricted to continuous 2D planar milling (Rectangles, Circles). Does not support complex 3D adaptive clearing or G-code specific to 3D printing (Slicing logic). | |
| - Always verify outputs using a visual G-Code parser prior to execution. | |