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Source Model Ablation (Trained-from-Scatch SAE)
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Source Model Ablation (Trained-from-Scatch SAE)

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Resa-STILL-Tina-3000-step (Trained-from-Scratch SAE)

Notes
Author
upup-ashton-wang
State
Finished
Start time
June 1st, 2025 9:50:21 AM
Runtime
18m 23s
Tracked hours
18m 22s
Run path
upup-ashton-wang-usc/Resa/8vmie0yi
OS
Linux-5.15.0-92-generic-x86_64-with-glibc2.35
Python version
CPython 3.10.16
Command
/home/omer/shangshang/workspace/reasoning/reasoning-sae/./scripts/train/run_sae_based_distill.py --config ./recipes/DeepSeek-R1-Distill-Qwen-1.5B/grpo/distill_curated_still.yaml --host_model_checkpoint checkpoint-3000 --student_model_name DeepSeek-R1-Distill-Qwen-1.5B --distill_dataset_name curated_still --distill_type sft_r1_distill --sae_hookpoint model.layers.12 --sae_type reason_pretrained
System Hardware
CPU count32
Logical CPU count 64
GPU count8
GPU typeNVIDIA RTX 6000 Ada Generation
W&B CLI Version
0.19.8
Group
Source Model Ablation (Trained-from-Scatch SAE)
Config

Config parameters are your model's inputs. Learn more

  • ▶
    {} 20 keys
    • "DeepSeek-R1-Distill-Qwen-1.5B"
    • 1
    • "curated_still"
    • "sft_r1_distill"
    • "checkpoint-3000"
    • "curated_still"
    • "grpo"
    • 0.000001
    • 1
    • 128
    • 0.05
    • 32
    • ▶
      [] 7 items
      • 2
      • "model.layers.12"
      • "sae-DeepSeek-R1-Distill-Qwen-1.5B-65k"
      • "reason_pretrained"
      • 500
      • 42
      • "DeepSeek-R1-Distill-Qwen-1.5B"
    Summary

    Summary metrics are your model's outputs. Learn more

    • ▶
      {} 5 keys
      • 1
      • 188.6456310679612
      • 0.000001
      • 2.21875
      • 258
    Artifact Outputs

    This run produced these artifacts as outputs. Total: 1. Learn more

    1-1
    of 1
    Type
    Name
    Consumer count
    wandb-history
    run-8vmie0yi-history:v0
    0
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