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 ronitraj/quantumscribe to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ronitraj/quantumscribe to start chattingLoad model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="ronitraj/quantumscribe",
max_seq_length=2048,
)Quick Links
QuantumScribe (GRPO LoRA)
LoRA adapter fine-tuned with GRPO for logical quantum error correction, on top of base unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit.
Adapter
- LoRA
r=16,lora_alpha=32,lora_dropout=0.1 - Target:
q_proj,k_proj,v_proj,o_proj(PEFT 0.18.1)
Training
- W&B: ronitraj/QuantumScribe-GRPO (e.g. run
4p7eurnc) - ~1500 GRPO steps; SFT warm-up as in the project
scripts/train_grpo.py
Eval (from project data/eval_grpo.json)
- Logical correction rate high (~0.96 on the recorded run)
- pymatching_beat reported at 0 on the evaluated split — align narrative and metrics (continuous vs threshold) with your harness and README
Load
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_id = "unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit"
adapter_id = "ronitraj/quantumscribe"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(
base_id, device_map="auto", trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter_id)
- Downloads last month
- 10
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 ronitraj/quantumscribe to start chatting