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mr9b-layeraudit-8node-215
aviralku/mr9b-layeraudit-8node-215
mr9b-layeraudit-8node-215 is a machine learning model from aviralku. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
Mid-training research checkpoints from a fully-async GRPO run with the pipelined frozen-E layer audit enabled (metareasoning.layeraudit.enable=true).
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Public
Updated Jul 21, 2026
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75.3 GB
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At a glance
- Library
- transformers
- License
- other
- Access
- Public
- Created
- Jul 20, 2026
- Updated
- Jul 21, 2026
- SHA
- 8be4a901
Model card
From the Hugging Face model README
mr9b meta-reasoning RL — layer-audit — 8-node/215 checkpoints
Mid-training research checkpoints from a fully-async GRPO run with the
pipelined frozen-E layer audit enabled (meta_reasoning.layer_audit.enable=true).
- Base: Qwen3.5 (~9B hybrid,
model_type: qwen3_5, 32 layers) - Method: fully-async GRPO meta-reasoning RL (VERL fully_async_policy)
- Layer audit: after each completed reasoning layer, a frozen executor audits it (classify explorations as new/refinement/verification/duplicate, diagnose tunnel vision + gaps) and injects one-step control feedback into the next MR step.
- Topology 215: 2 trainer / 1 rollout / 5 frozen-E nodes (8 nodes, TP8/PP1/DP2)
- Experiment:
mr9b_layeraudit_8node_215_20260718_231300 - W&B project:
meta_reason_rl_h100_5node
Contents
Each subfolder is a standalone HF model (weights + tokenizer + chat template):
global_step_68/global_step_69/
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("aviralku/mr9b-layeraudit-8node-215", subfolder="global_step_69")
t = AutoTokenizer.from_pretrained("aviralku/mr9b-layeraudit-8node-215", subfolder="global_step_69")
Private research checkpoints; not an official release.
- Library
- transformers
- License
- other
- Created
- Jul 20, 2026
- Updated
- Jul 21, 2026