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+ commit: 89bc64f41f54ca1f08c9d90c5c2ea13f98123c1b
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+ Pruning: keeping 6339/16384 neurons (threshold=2e-05)
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run-20260503_053748-vjc8361x/files/output.log ADDED
@@ -0,0 +1,114 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Attack dataset: 40000 train samples (physics)
2
+
3
+ ================================================================================
4
+ Baseline LLM judge eval (attack/google--gemma-3-12b-it/layer_31--width_16k--canonical/biology/h2e-05/k6339/physics)
5
+ ================================================================================
6
+ (running with pruned SAE hooked at model.language_model.layers.31)
7
+ Getting token length estimate... w/ batch_size=1024
8
+ Tokenizing batches for length estimate w/ batch_size=1024: 100%|██████████████████████████████████████████| 1/1 [00:00<00:00, 85.21it/s]
9
+ DONE Getting token length estimate!
10
+ Getting length-aware padding breakpoints...
11
+ DONE Tokenizing...
12
+ Tokenizing texts into BEs: 100%|████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 1367.56it/s]
13
+ Generating responses...: 100%|████████████████████████████████████████████████████████████████████████████| 9/9 [06:14<00:00, 41.65s/it]
14
+ [LLM judge] Saved 600 judge inputs to /root/SAEScoping/experiments/outputs_scoping/google--gemma-3-12b-it/layer_31--width_16k--canonical/biology/h2e-05/k6339/6989u00w/attack/physics/vjc8361x/llm_judge_csvs/judge_inputs_0000.json
15
+ Running LLM judges...: 100%|██████████████████████████████████████████████████████████████████████████| 600/600 [00:23<00:00, 25.25it/s]
16
+ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
17
+ Baseline scores:
18
+ llm_judge/biology/in_scope/fluency: 0.9450
19
+ llm_judge/biology/in_scope/ground_truth_similarity: 0.6750
20
+ llm_judge/biology/in_scope/quality: 0.8617
21
+ llm_judge/biology/in_scope/relevance: 0.9650
22
+ llm_judge/physics/attack_scope/fluency: 0.6850
23
+ llm_judge/physics/attack_scope/ground_truth_similarity: 0.3750
24
+ llm_judge/physics/attack_scope/quality: 0.6533
25
+ llm_judge/physics/attack_scope/relevance: 0.9000
26
+ @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
27
+ Saved to /root/SAEScoping/experiments/outputs_scoping/google--gemma-3-12b-it/layer_31--width_16k--canonical/biology/h2e-05/k6339/6989u00w/attack/physics/vjc8361x/llm_judge_csvs/baseline_pre_attack.csv and /root/SAEScoping/experiments/outputs_scoping/google--gemma-3-12b-it/layer_31--width_16k--canonical/biology/h2e-05/k6339/6989u00w/attack/physics/vjc8361x/llm_judge_csvs/baseline_pre_attack.scores.json
28
+ Params @ hookpoint=model.language_model.layers.31: 208 trainable, 857 frozen, frozen layers=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]
29
+ Adding EOS to train dataset: 100%|██████████████████████████████████████████████████████| 40000/40000 [00:01<00:00, 37203.31 examples/s]
30
+ Tokenizing train dataset: 100%|██████████████████████████████████████████████████████████| 40000/40000 [00:19<00:00, 2101.14 examples/s]
31
+ Truncating train dataset: 100%|████████████████████████████████████████████████████████| 40000/40000 [00:00<00:00, 362899.70 examples/s]
32
+ Adding EOS to biology dataset: 100%|████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 34304.75 examples/s]
33
+ Tokenizing biology dataset: 100%|████████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 2556.85 examples/s]
34
+ Truncating biology dataset: 100%|██████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 403298.46 examples/s]
35
+ Adding EOS to physics dataset: 100%|████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 36428.49 examples/s]
36
+ Tokenizing physics dataset: 100%|████████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 2039.78 examples/s]
37
+ Truncating physics dataset: 100%|██████████████████████████████████████████████████████████| 500/500 [00:00<00:00, 341778.36 examples/s]
38
+ 0%| | 0/10000 [00:00<?, ?it/s]Traceback (most recent call last):
39
+ File "/root/SAEScoping/experiments/script_scoping_pipeline_stemqa.py", line 1088, in <module>
40
+ main()
41
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/click/core.py", line 1442, in __call__
42
+ return self.main(*args, **kwargs)
43
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^
44
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/click/core.py", line 1363, in main
45
+ rv = self.invoke(ctx)
46
+ ^^^^^^^^^^^^^^^^
47
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/click/core.py", line 1226, in invoke
48
+ return ctx.invoke(self.callback, **ctx.params)
49
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
50
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/click/core.py", line 794, in invoke
51
+ return callback(*args, **kwargs)
52
+ ^^^^^^^^^^^^^^^^^^^^^^^^^
53
+ File "/root/SAEScoping/experiments/script_scoping_pipeline_stemqa.py", line 1029, in main
54
+ stage_train(
55
+ File "/root/SAEScoping/experiments/script_scoping_pipeline_stemqa.py", line 403, in stage_train
56
+ train_sae_enhanced_model(
57
+ File "/root/SAEScoping/sae_scoping/trainers/sae_enhanced/train.py", line 323, in train_sae_enhanced_model
58
+ trainer.train()
59
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/transformers/trainer.py", line 2328, in train
60
+ return inner_training_loop(
61
+ ^^^^^^^^^^^^^^^^^^^^
62
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/transformers/trainer.py", line 2738, in _inner_training_loop
63
+ self.optimizer.step()
64
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/accelerate/optimizer.py", line 179, in step
65
+ self.optimizer.step(closure)
66
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/torch/optim/lr_scheduler.py", line 166, in wrapper
67
+ return func.__get__(opt, opt.__class__)(*args, **kwargs)
68
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
69
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/torch/optim/optimizer.py", line 533, in wrapper
70
+ out = func(*args, **kwargs)
71
+ ^^^^^^^^^^^^^^^^^^^^^
72
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
73
+ return func(*args, **kwargs)
74
+ ^^^^^^^^^^^^^^^^^^^^^
75
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/bitsandbytes/optim/optimizer.py", line 325, in step
76
+ self.init_state(group, p, gindex, pindex)
77
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
78
+ return func(*args, **kwargs)
79
+ ^^^^^^^^^^^^^^^^^^^^^
80
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/bitsandbytes/optim/optimizer.py", line 507, in init_state
81
+ state["state1"] = self.get_state_buffer(p, dtype=torch.uint8)
82
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
83
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/bitsandbytes/optim/optimizer.py", line 368, in get_state_buffer
84
+ buff = F.get_paged(*p.shape, dtype=dtype, device=p.device)
85
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
86
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/bitsandbytes/functional.py", line 136, in get_paged
87
+ cuda_ptr = lib.cget_managed_ptr(ct.c_size_t(num_bytes))
88
+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
89
+ File "/root/miniconda3/envs/sae/lib/python3.12/site-packages/bitsandbytes/cextension.py", line 269, in throw_on_call
90
+ raise RuntimeError(f"{self.formatted_error}Native code method attempted to call: lib.{name}()")
91
+ RuntimeError:
92
+ 🚨 CUDA SETUP ERROR: Missing dependency: libnvJitLink.so.13 🚨
93
+
94
+ CUDA 13.x runtime libraries were not found in the LD_LIBRARY_PATH.
95
+
96
+ To fix this, make sure that:
97
+ 1. You have installed CUDA 13.x toolkit on your system
98
+ 2. The CUDA runtime libraries are in your LD_LIBRARY_PATH
99
+
100
+ You can add them with (and persist the change by adding the line to your .bashrc):
101
+ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/path/to/cuda-13.x/ lib64
102
+
103
+ Original error: libnvJitLink.so.13: cannot open shared object file: No such file or directory
104
+
105
+ 🔍 Run this command for detailed diagnostics:
106
+ python -m bitsandbytes
107
+
108
+ If you've tried everything and still have issues:
109
+ 1. Include ALL version info (operating system, bitsandbytes, pytorch, cuda, python)
110
+ 2. Describe what you've tried in detail
111
+ 3. Open an issue with this information:
112
+ https://github.com/bitsandbytes-foundation/bitsandbytes/issues
113
+
114
+ Native code method attempted to call: lib.cget_managed_ptr()
run-20260503_053748-vjc8361x/files/requirements.txt ADDED
@@ -0,0 +1,234 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ packaging==26.0
2
+ wheel==0.46.3
3
+ pip==26.0.1
4
+ webencodings==0.5.1
5
+ pytz==2026.1.post1
6
+ pure_eval==0.2.3
7
+ ptyprocess==0.7.0
8
+ nvidia-cusparselt-cu12==0.6.3
9
+ mpmath==1.3.0
10
+ i2==0.1.63
11
+ fastjsonschema==2.21.2
12
+ better-abc==0.0.3
13
+ zipp==3.23.1
14
+ xxhash==3.7.0
15
+ websocket-client==1.9.0
16
+ webcolors==25.10.0
17
+ wcwidth==0.7.0
18
+ wadler_lindig==0.1.7
19
+ urllib3==2.6.3
20
+ uri-template==1.3.0
21
+ tzdata==2026.2
22
+ typing_extensions==4.15.0
23
+ traitlets==5.14.3
24
+ tqdm==4.67.3
25
+ tornado==6.5.5
26
+ tinycss2==1.4.0
27
+ threadpoolctl==3.6.0
28
+ tenacity==9.1.4
29
+ sympy==1.14.0
30
+ soupsieve==2.8.3
31
+ sniffio==1.3.1
32
+ smmap==5.0.3
33
+ six==1.17.0
34
+ sentencepiece==0.2.1
35
+ Send2Trash==2.1.0
36
+ safetensors==0.5.3
37
+ ruff==0.14.2
38
+ rpds-py==0.30.0
39
+ rfc3986-validator==0.1.1
40
+ regex==2026.4.4
41
+ pyzmq==27.1.0
42
+ PyYAML==6.0.3
43
+ python-json-logger==4.1.0
44
+ python-dotenv==1.2.2
45
+ pyparsing==3.3.2
46
+ Pygments==2.20.0
47
+ pycparser==3.0
48
+ pyarrow==24.0.0
49
+ psutil==7.2.2
50
+ protobuf==6.33.6
51
+ propcache==0.4.1
52
+ prometheus_client==0.25.0
53
+ pluggy==1.6.0
54
+ platformdirs==4.9.6
55
+ pillow==12.2.0
56
+ pexpect==4.9.0
57
+ parso==0.8.7
58
+ pandocfilters==1.5.1
59
+ orjson==3.11.0
60
+ nvidia-nvtx-cu12==12.6.77
61
+ nvidia-nvjitlink-cu12==12.6.85
62
+ nvidia-nccl-cu12==2.26.2
63
+ nvidia-curand-cu12==10.3.7.77
64
+ nvidia-cufile-cu12==1.11.1.6
65
+ nvidia-cuda-runtime-cu12==12.6.77
66
+ nvidia-cuda-nvrtc-cu12==12.6.77
67
+ nvidia-cuda-cupti-cu12==12.6.80
68
+ nvidia-cublas-cu12==12.6.4.1
69
+ numpy==1.26.4
70
+ networkx==3.6.1
71
+ nest-asyncio==1.6.0
72
+ natsort==8.4.0
73
+ narwhals==2.20.0
74
+ multidict==6.7.1
75
+ mistune==3.2.0
76
+ mdurl==0.1.2
77
+ MarkupSafe==3.0.3
78
+ lark==1.3.1
79
+ kiwisolver==1.5.0
80
+ jupyterlab_pygments==0.3.0
81
+ jsonpointer==3.1.1
82
+ json5==0.14.0
83
+ joblib==1.5.3
84
+ jiter==0.14.0
85
+ iniconfig==2.3.0
86
+ importlib_resources==7.1.0
87
+ idna==3.13
88
+ hf-xet==1.4.3
89
+ h11==0.16.0
90
+ fsspec==2025.3.0
91
+ frozenlist==1.8.0
92
+ fqdn==1.5.1
93
+ fonttools==4.62.1
94
+ filelock==3.29.0
95
+ fancy-einsum==0.0.3
96
+ executing==2.2.1
97
+ einops==0.8.2
98
+ dol==0.3.41
99
+ docstring_parser==0.18.0
100
+ distro==1.9.0
101
+ dill==0.3.8
102
+ defusedxml==0.7.1
103
+ decorator==5.2.1
104
+ debugpy==1.8.20
105
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106
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107
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108
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109
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110
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111
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112
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113
+ attrs==26.1.0
114
+ async-lru==2.3.0
115
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116
+ annotated-types==0.7.0
117
+ aiohappyeyeballs==2.6.1
118
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119
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120
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121
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122
+ stack-data==0.6.3
123
+ simple-parsing==0.1.8
124
+ sentry-sdk==2.58.0
125
+ scipy==1.17.1
126
+ rfc3987-syntax==1.1.0
127
+ rfc3339-validator==0.1.4
128
+ requests==2.32.4
129
+ referencing==0.37.0
130
+ python-dateutil==2.9.0.post0
131
+ pytest==8.4.1
132
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133
+ prompt_toolkit==3.0.52
134
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135
+ patsy==1.0.2
136
+ nvidia-cusparse-cu12==12.5.4.2
137
+ nvidia-cufft-cu12==11.3.0.4
138
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139
+ nltk==3.9.4
140
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141
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142
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143
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144
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145
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146
+ jaxtyping==0.3.2
147
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148
+ importlib_metadata==9.0.0
149
+ httpcore==1.0.9
150
+ gitdb==4.0.12
151
+ contourpy==1.3.3
152
+ config2py==0.1.47
153
+ cffi==2.0.0
154
+ beautifulsoup4==4.14.3
155
+ anyio==4.13.0
156
+ aiosignal==1.4.0
157
+ tiktoken==0.12.0
158
+ scikit-learn==1.7.1
159
+ rich==15.0.0
160
+ pydantic==2.12.4
161
+ py2store==0.1.22
162
+ pandas==2.3.1
163
+ nvidia-cusolver-cu12==11.7.1.2
164
+ matplotlib==3.10.9
165
+ jupyter_server_terminals==0.5.4
166
+ jupyter_client==8.8.0
167
+ jsonschema-specifications==2025.9.1
168
+ ipython==9.4.0
169
+ huggingface_hub==0.36.2
170
+ httpx==0.28.1
171
+ graze==0.1.39
172
+ GitPython==3.1.49
173
+ arrow==1.4.0
174
+ argon2-cffi-bindings==25.1.0
175
+ aiohttp==3.13.5
176
+ wandb==0.21.0
177
+ tokenizers==0.22.2
178
+ statsmodels==0.14.6
179
+ seaborn==0.13.2
180
+ openai==2.33.0
181
+ jsonschema==4.26.0
182
+ isoduration==20.11.0
183
+ ipykernel==7.2.0
184
+ babe==0.0.7
185
+ argon2-cffi==25.1.0
186
+ transformers==4.56.1
187
+ schedulefree==1.4.1
188
+ plotly-express==0.4.1
189
+ nbformat==5.10.4
190
+ litellm==1.74.7
191
+ datasets==4.0.0
192
+ accelerate==1.13.0
193
+ trl==0.22.2
194
+ transformers-stream-generator==0.0.5
195
+ peft==0.16.0
196
+ nbclient==0.10.4
197
+ jupyter-events==0.12.1
198
+ eai-sparsify==1.3.0
199
+ transformer-lens==2.17.0
200
+ nbconvert==7.17.1
201
+ nvidia-cusparse==12.6.3.3
202
+ jupyter_server==2.17.0
203
+ notebook_shim==0.2.4
204
+ jupyterlab_server==2.28.0
205
+ jupyter-lsp==2.3.1
206
+ jupyterlab==4.4.10
207
+ notebook==7.4.4
208
+ sae-scoping==2020.0.0
209
+ mypy_extensions==1.1.0
210
+ typing-inspect==0.9.0
211
+ pandera==0.31.1
212
+ bitsandbytes==0.49.2
213
+ hf_transfer==0.1.9
214
+ nvidia-cufft==12.0.0.61
215
+ sae-lens==6.43.0
216
+ nvidia-cusparselt-cu13==0.8.0
217
+ cuda-toolkit==13.0.2
218
+ triton==3.6.0
219
+ cuda-bindings==13.2.0
220
+ setuptools==81.0.0
221
+ nvidia-nvtx==13.0.85
222
+ nvidia-nvshmem-cu13==3.4.5
223
+ nvidia-nvjitlink==13.0.88
224
+ nvidia-nccl-cu13==2.28.9
225
+ nvidia-curand==10.4.0.35
226
+ nvidia-cufile==1.15.1.6
227
+ nvidia-cuda-runtime==13.0.96
228
+ nvidia-cuda-nvrtc==13.0.88
229
+ nvidia-cuda-cupti==13.0.85
230
+ nvidia-cublas==13.1.0.3
231
+ cuda-pathfinder==1.5.4
232
+ nvidia-cudnn-cu13==9.19.0.56
233
+ nvidia-cusolver==12.0.4.66
234
+ torch==2.11.0
run-20260503_053748-vjc8361x/files/wandb-metadata.json ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "os": "Linux-6.8.0-106-generic-x86_64-with-glibc2.39",
3
+ "python": "CPython 3.12.13",
4
+ "startedAt": "2026-05-03T05:37:48.171009Z",
5
+ "args": [
6
+ "--train-domain",
7
+ "biology",
8
+ "--attack-domain",
9
+ "physics",
10
+ "--stage",
11
+ "attack",
12
+ "--hf-recover-repo",
13
+ "roonbug/2b63aec8",
14
+ "--dev",
15
+ "--device",
16
+ "cuda",
17
+ "--firing-rate-threshold",
18
+ "2e-5",
19
+ "--gemma3",
20
+ "--max-steps-attack",
21
+ "10000"
22
+ ],
23
+ "program": "/root/SAEScoping/experiments/script_scoping_pipeline_stemqa.py",
24
+ "codePath": "experiments/script_scoping_pipeline_stemqa.py",
25
+ "codePathLocal": "experiments/script_scoping_pipeline_stemqa.py",
26
+ "git": {
27
+ "remote": "git@github.com:4gatepylon/SAEScoping.git",
28
+ "commit": "89bc64f41f54ca1f08c9d90c5c2ea13f98123c1b"
29
+ },
30
+ "email": "aruna.evam@gmail.com",
31
+ "root": "/root/SAEScoping",
32
+ "host": "305dff266a17",
33
+ "executable": "/root/miniconda3/envs/sae/bin/python",
34
+ "cpu_count": 64,
35
+ "cpu_count_logical": 128,
36
+ "gpu": "NVIDIA RTX PRO 6000 Blackwell Server Edition",
37
+ "gpu_count": 1,
38
+ "disk": {
39
+ "/": {
40
+ "total": "107374182400",
41
+ "used": "9989619712"
42
+ }
43
+ },
44
+ "memory": {
45
+ "total": "1622884343808"
46
+ },
47
+ "gpu_nvidia": [
48
+ {
49
+ "name": "NVIDIA RTX PRO 6000 Blackwell Server Edition",
50
+ "memoryTotal": "102641958912",
51
+ "cudaCores": 24064,
52
+ "architecture": "Blackwell",
53
+ "uuid": "GPU-7c34c3f0-be50-1999-a922-ffbfa212e2da"
54
+ }
55
+ ],
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+ "cudaVersion": "13.0",
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+ "writerId": "4cm3mzfydp843aw35eswianerjxz65yv"
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+ }
run-20260503_053748-vjc8361x/files/wandb-summary.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"llm_judge/physics/attack_scope/relevance_pre_attack":0.9,"pre-attack-baseline/llm_judge/biology/in_scope/relevance":0.965,"llm_judge/biology/in_scope/relevance_pre_attack":0.965,"llm_judge/biology/in_scope/quality_pre_attack":0.8616666666666667,"llm_judge/physics/attack_scope/ground_truth_similarity_pre_attack":0.375,"_step":1,"_wandb":{"runtime":432},"pre-attack-baseline/llm_judge/physics/attack_scope/fluency":0.685,"llm_judge/physics/attack_scope/fluency_pre_attack":0.685,"trainer/global_step":0,"llm_judge/biology/in_scope/ground_truth_similarity_pre_attack":0.675,"pre-attack-baseline/llm_judge/biology/in_scope/fluency":0.945,"pre-attack-baseline/llm_judge/physics/attack_scope/quality":0.6533333333333333,"llm_judge/biology/in_scope/fluency_pre_attack":0.945,"pre-attack-baseline/llm_judge/biology/in_scope/quality":0.8616666666666667,"_runtime":432,"pre-attack-baseline/llm_judge/physics/attack_scope/ground_truth_similarity":0.375,"pre-attack-baseline/llm_judge/biology/in_scope/ground_truth_similarity":0.675,"pre-attack-baseline/llm_judge/physics/attack_scope/relevance":0.9,"_timestamp":1.7777870675259757e+09,"llm_judge/physics/attack_scope/quality_pre_attack":0.6533333333333333}
run-20260503_053748-vjc8361x/logs/debug-core.log ADDED
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run-20260503_053748-vjc8361x/logs/debug-internal.log ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
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