Instructions to use squaredcuber/forge-optimizer-qwen3.6-35b-a3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use squaredcuber/forge-optimizer-qwen3.6-35b-a3b 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 squaredcuber/forge-optimizer-qwen3.6-35b-a3b 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 squaredcuber/forge-optimizer-qwen3.6-35b-a3b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for squaredcuber/forge-optimizer-qwen3.6-35b-a3b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="squaredcuber/forge-optimizer-qwen3.6-35b-a3b", max_seq_length=2048, )
forge-optimizer
A LoRA fine-tune of Qwen3.6-35B-A3B (MoE) that turns unoptimized backend code into efficient, correct code — the skill measured by forger-bench, an efficiency-aware benchmark for AI-generated InsForge SDK code.
Trained with Unsloth (bf16 LoRA) + an agentic GRPO loop adopting CUDA-Agent (arXiv 2602.24286): the model writes a solution, the forger-bench grader runs+verifies+ measures real server metrics, and a discrete milestone reward (-1 incorrect/scaleBug, 1 wasteful, 2 beats-naive, 3 near-optimal) drives RL.
Contamination control
Never trained on a sealed test task; held-out concepts measure optimization skill vs template memorization.
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