R2Flow-9B

The trained R² Flow Supervisor as a complete model: the trained LoRA adapter (rank 4, alpha 8 on q_proj, k_proj, v_proj, o_proj, out_proj, gate_proj, up_proj and down_proj) is merged into the Qwen3.5-9B weights.

The merged layers are stored in float32. The LoRA update is about 0.1 to 0.3% of the weight norm, and rounding the merged layers to bfloat16 would discard most of it, so load the model in float32 to reproduce the trained Supervisor exactly; all other layers are the unchanged bfloat16 base weights.

import torch
from transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration

model = Qwen3_5ForConditionalGeneration.from_pretrained("beita6969/R2Flow-9b", dtype=torch.float32)
tokenizer = AutoTokenizer.from_pretrained("beita6969/R2Flow-9b")