Instructions to use decoded-cipher/nodrix-coder-1.5b-lora-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use decoded-cipher/nodrix-coder-1.5b-lora-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "decoded-cipher/nodrix-coder-1.5b-lora-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use decoded-cipher/nodrix-coder-1.5b-lora-v1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for decoded-cipher/nodrix-coder-1.5b-lora-v1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for decoded-cipher/nodrix-coder-1.5b-lora-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for decoded-cipher/nodrix-coder-1.5b-lora-v1 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="decoded-cipher/nodrix-coder-1.5b-lora-v1", max_seq_length=2048, )
| base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct | |
| library_name: peft | |
| license: apache-2.0 | |
| tags: [lora, esp32, arduino, nodrix, unsloth] | |
| # Nodrix build assistant — LoRA adapter (v1-qwen1.5b) | |
| LoRA adapter fine-tuning `Qwen/Qwen2.5-Coder-1.5B-Instruct` into an assistant for the Nodrix ESP32/Arduino library. | |
| Trained in Unsloth Studio (MLX, Apple Silicon). LoRA r=16, alpha=16, all linear modules, | |
| seq 512, 3 epochs / 63 steps, 167 training examples. | |
| First smoke run. Fixed *form* (real C++, prose-vs-code routing) but not facts. No eval set attached. | |
| ## Load | |
| ```python | |
| from peft import PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| base = "Qwen/Qwen2.5-Coder-1.5B-Instruct" | |
| tok = AutoTokenizer.from_pretrained(base) | |
| model = AutoModelForCausalLM.from_pretrained(base) | |
| model = PeftModel.from_pretrained(model, "decoded-cipher/nodrix-coder-1.5b-lora-v1") | |
| ``` | |
| System prompt used in training: | |
| > You are the Nodrix build assistant. You help ESP32 and Arduino developers build | |
| > projects with the Nodrix library. Use only real Nodrix APIs. | |
| License: Apache-2.0 (inherited from the base model). | |