Instructions to use decoded-cipher/nodrix-coder-7b-lora-v2 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-v2 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-v2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use decoded-cipher/nodrix-coder-7b-lora-v2 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-7b-lora-v2 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-7b-lora-v2 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-7b-lora-v2 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-7b-lora-v2", max_seq_length=2048, )
File size: 1,121 Bytes
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
license: apache-2.0
tags: [lora, esp32, arduino, nodrix, unsloth]
---
# Nodrix build assistant — LoRA adapter (v2-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, 3 epochs / 69 steps, 183 training examples.
Added 20 downlink examples. Fixed `setCACert` but overfit past ~step 24 (eval floor ~2.04). Superseded by v3.
## Load
```python
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-v2")
```
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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