How to use from
Unsloth StudioInstall 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 chattingUsing 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 chattingLoad 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,
)Quick Links
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
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).
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Model tree for decoded-cipher/nodrix-coder-1.5b-lora-v1
Base model
Qwen/Qwen2.5-1.5B Finetuned
Qwen/Qwen2.5-Coder-1.5B Finetuned
Qwen/Qwen2.5-Coder-1.5B-Instruct
Install Unsloth Studio (macOS, Linux, WSL)
# 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