Instructions to use mewbot97/Intent2Action-glimmer-programs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mewbot97/Intent2Action-glimmer-programs with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-models/Muse-Glimmer-30B") model = PeftModel.from_pretrained(base_model, "mewbot97/Intent2Action-glimmer-programs") - Notebooks
- Google Colab
- Kaggle
Rename to Intent2Action
Browse files
README.md
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tags: [robotics, program-synthesis, lora, qlora, manipulation]
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#
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A QLoRA adapter for [Muse Glimmer 30B](https://huggingface.co/meta-models/Muse-Glimmer-30B). Given a task intent
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(explicit rules such as "every cube into the red box") and the robot's object list, it writes a short, checkable
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Python program against the robot API of [
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`find_all`, `pick`, `place`, `verify`, and so on. The program is checked, dry-run and executed by the robot stack.
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The model never issues motions itself.
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tags: [robotics, program-synthesis, lora, qlora, manipulation]
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---
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# Intent2Action 路 Glimmer program writer (G1b)
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A QLoRA adapter for [Muse Glimmer 30B](https://huggingface.co/meta-models/Muse-Glimmer-30B). Given a task intent
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(explicit rules such as "every cube into the red box") and the robot's object list, it writes a short, checkable
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Python program against the robot API of [Intent2Action](https://github.com/allwinlucious/Intent2Action): `find`,
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`find_all`, `pick`, `place`, `verify`, and so on. The program is checked, dry-run and executed by the robot stack.
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The model never issues motions itself.
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