Instructions to use decoded-cipher/nodrix-coder-7b-lora-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use decoded-cipher/nodrix-coder-7b-lora-v3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "decoded-cipher/nodrix-coder-7b-lora-v3") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Nodrix build assistant β LoRA adapter (v3-qwen7b)
LoRA adapter fine-tuning Qwen/Qwen2.5-Coder-7B-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, 2 epochs / 50 steps, 193 training examples.
The good one. Cured the NODRIX_WRITE prior-conflict (generalized to held-out prompts). Residual: still defaults to WebSocket mode for battery, still invents some platform APIs β the RAG seam.
Load
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "Qwen/Qwen2.5-Coder-7B-Instruct"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base)
model = PeftModel.from_pretrained(model, "decoded-cipher/nodrix-coder-7b-lora-v3")
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).
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