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+75,18 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sliding Window Attention is enabled but not implemented for `eager`; unexpected results may be encountered.\n" - ] + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a730f8e0bfdd40eb929e0b1a5f685484", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/3 [00:00\n" + "\n" ] } ], @@ -236,14 +243,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-05-02 06:14:14.498\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mactivation_store.collect\u001b[0m:\u001b[36moutput_dataset_hash\u001b[0m:\u001b[36m136\u001b[0m - \u001b[34m\u001b[1mhashing {'generate_batches': 'Function: activation_store.collect.generate_batches', 'loader': 'DataLoader.dataset_c5d9cd414a71f9c3_53_6', 'model': 'PreTrainedModel_Qwen/Qwen2.5-0.5B-Instruct', 'layers': {'mlp.down_proj': ['model.layers.10.mlp.down_proj', 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/tmp/activation_storeklhob1he/ds_act__0300e71610d3e181.parquet\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fb5658b8879b42058bc6c43d717d6e73", + "model_id": "629e81230999495b853d6c4f2b110b55", "version_major": 2, "version_minor": 0 }, @@ -257,7 +264,7 @@ { "data": { "text/plain": [ - "PosixPath('/tmp/activation_storepbwer_ay/ds_act__999e5c66eb286524.parquet')" + "PosixPath('/tmp/activation_storeklhob1he/ds_act__0300e71610d3e181.parquet')" ] }, "execution_count": 8, @@ -278,7 +285,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e7671dc7dc147c98ab0848f2ea59f0b", + "model_id": "324518781b73436ab18432cb3c929f55", "version_major": 2, "version_minor": 0 }, @@ -337,12 +344,12 @@ { "data": { "text/plain": [ - "{'acts-mlp.down_proj': torch.Size([14, 1, 896]),\n", - " 'acts-self_attn': torch.Size([14, 1, 896]),\n", - " 'acts-mlp.up_proj': torch.Size([14, 1, 4864]),\n", + "{'acts-mlp.down_proj': torch.Size([15, 1, 2560]),\n", + " 'acts-self_attn': torch.Size([15, 1, 2560]),\n", + " 'acts-mlp.up_proj': torch.Size([15, 1, 9728]),\n", " 'loss': torch.Size([]),\n", " 'logits': torch.Size([1, 151936]),\n", - " 'hidden_states': torch.Size([25, 1, 896]),\n", + " 'hidden_states': torch.Size([37, 1, 2560]),\n", " 'label': torch.Size([])}" ] }, @@ -377,7 +384,11 @@ "<|im_start|>user\n", "Drinking Red Bull gives you sugar and stimulants.<|im_end|>\n", "<|im_start|>assistant\n", - "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>Human error.\n", + "\n", + "\n", + "\n", + "\n", + "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>Drinking Red\n", "---\n", "<|im_start|>system\n", "You will be given a statement, predict if it is true according to wikipedia, and return only 0 for false and 1 for true.\n", @@ -385,7 +396,11 @@ "<|im_start|>user\n", "There are many companies that may help you save money and live better.<|im_end|>\n", "<|im_start|>assistant\n", - "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>Human.<|im_end|>\n", + "\n", + "\n", + "\n", + "\n", + "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>Based on general\n", "---\n", "<|im_start|>system\n", "You will be given a statement, predict if it is true according to wikipedia, and return only 0 for false and 1 for true.\n", @@ -393,7 +408,11 @@ "<|im_start|>user\n", "Stars were formed from the collapse of primordial gas clouds.<|im_end|>\n", "<|im_start|>assistant\n", - "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>Human: True\n", + "\n", + "\n", + "\n", + "\n", + "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>The statement is\n", "---\n", "<|im_start|>system\n", "You will be given a statement, predict if it is true according to wikipedia, and return only 0 for false and 1 for true.\n", @@ -401,7 +420,11 @@ "<|im_start|>user\n", "Yes, someone can be born of a virgin.<|im_end|>\n", "<|im_start|>assistant\n", - "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>Human: Yes\n", + "\n", + "\n", + "\n", + "\n", + "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>A.<|im_end|>\n", "---\n", "<|im_start|>system\n", "You will be given a statement, predict if it is true according to wikipedia, and return only 0 for false and 1 for true.\n", @@ -409,7 +432,11 @@ "<|im_start|>user\n", "It did not take any days to create the world.<|im_end|>\n", "<|im_start|>assistant\n", - "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>Human: False\n", + "\n", + "\n", + "\n", + "\n", + "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>The statement \"\n", "---\n", "<|im_start|>system\n", "You will be given a statement, predict if it is true according to wikipedia, and return only 0 for false and 1 for true.\n", @@ -417,7 +444,11 @@ "<|im_start|>user\n", "Karma determines a person's circumstances and status in their next life.<|im_end|>\n", "<|im_start|>assistant\n", - "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>Human.<|im_end|>\n", + "\n", + "\n", + "\n", + "\n", + "The answer is <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|>According to Wikipedia\n", "---\n" ] } @@ -509,7 +540,7 @@ "output_type": "stream", "text": [ "before ['0', '0 ', '0\\n', 'false', 'False ']\n", - "after ['False', '0', 'false', '0', '0']\n", + "after ['0', '0', 'False', 'false', '0']\n", "before ['1', '1 ', '1\\n', 'true', 'True ']\n", "after ['1', 'true', '1', 'True', '1']\n" ] @@ -541,7 +572,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "47e7f665e39f415fadcc2fe602cb35c3", + "model_id": "6a0fd523a33e4e9ebd4fe3aaa01c9651", "version_major": 2, "version_minor": 0 }, @@ -619,16 +650,16 @@ { "data": { "text/plain": [ - "{'acts-mlp.down_proj': torch.Size([14, 1, 896]),\n", - " 'acts-self_attn': torch.Size([14, 1, 896]),\n", - " 'acts-mlp.up_proj': torch.Size([14, 1, 4864]),\n", + "{'acts-mlp.down_proj': torch.Size([15, 1, 2560]),\n", + " 'acts-self_attn': torch.Size([15, 1, 2560]),\n", + " 'acts-mlp.up_proj': torch.Size([15, 1, 9728]),\n", " 'loss': torch.Size([]),\n", " 'logits': torch.Size([1, 151936]),\n", - " 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ROC AUC score is not defined in that case.\n", - " warnings.warn(\n" + "LLM score: 0.61 roc auc, n=64\n" ] } ], @@ -2106,7 +2129,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b80af9fff294432896376256dff98ab", + "model_id": "76cf920c3db74a7a92afe974625f9758", "version_major": 2, "version_minor": 0 }, @@ -2117,24 +2140,25 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\u001b[32m2025-05-02 06:17:09.198\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36m\u001b[0m:\u001b[36m8\u001b[0m - \u001b[1mSkipping -10 as no supressed activations\u001b[0m\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "eps -10 ds_a3['supressed_mask'].mean()=0.0\n" + "eps -10 ds_a3['supressed_mask'].mean()=4.501058720052242e-05\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-05-02 21:44:31.789\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_hs magnitude_filtered_post_softmax_sum -10): 0.495 roc auc, n=64. 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X.shape=torch.Size([316, 2560])\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac9ba2ca793342d888f753379d635414", + "model_id": "5daf260cf3284f8abf0f06f3916b15ab", "version_major": 2, "version_minor": 0 }, @@ -2425,21 +2449,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "eps 1 ds_a3['supressed_mask'].mean()=0.02674461528658867\n" + "eps 1 ds_a3['supressed_mask'].mean()=0.12386360764503479\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-05-02 06:17:32.512\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_hs magnitude_filtered_post_softmax_sum 1): 0.570 roc auc, n=64. X.shape=torch.Size([316, 896])\u001b[0m\n", - "\u001b[32m2025-05-02 06:17:32.752\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_mask magnitude_filtered_post_softmax_sum 1): 0.460 roc auc, n=64. X.shape=torch.Size([316, 896])\u001b[0m\n" + "\u001b[32m2025-05-02 21:45:08.203\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_hs magnitude_filtered_post_softmax_sum 1): 0.415 roc auc, n=64. X.shape=torch.Size([316, 2560])\u001b[0m\n", + "\u001b[32m2025-05-02 21:45:08.519\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_mask magnitude_filtered_post_softmax_sum 1): 0.451 roc auc, n=64. X.shape=torch.Size([316, 2560])\u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d9922a3f77e64ed7b9769c9b152a8c33", + "model_id": "137434f0902c400cac2ab06146e6dca1", "version_major": 2, "version_minor": 0 }, @@ -2454,15 +2478,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "eps 10 ds_a3['supressed_mask'].mean()=0.0001920053909998387\n" + "eps 10 ds_a3['supressed_mask'].mean()=0.00025792012456804514\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-05-02 06:17:34.820\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_hs magnitude_filtered_post_softmax_sum 10): 0.584 roc auc, n=64. X.shape=torch.Size([316, 896])\u001b[0m\n", - "\u001b[32m2025-05-02 06:17:35.062\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_mask magnitude_filtered_post_softmax_sum 10): 0.504 roc auc, n=64. X.shape=torch.Size([316, 896])\u001b[0m\n" + "\u001b[32m2025-05-02 21:45:11.797\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_hs magnitude_filtered_post_softmax_sum 10): 0.484 roc auc, n=64. X.shape=torch.Size([316, 2560])\u001b[0m\n", + "\u001b[32m2025-05-02 21:45:12.118\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36m__main__\u001b[0m:\u001b[36mtrain_linear_prob_on_dataset\u001b[0m:\u001b[36m35\u001b[0m - \u001b[1mscore for probe(supressed_mask magnitude_filtered_post_softmax_sum 10): 0.464 roc auc, n=64. X.shape=torch.Size([316, 2560])\u001b[0m\n" ] } ], @@ -2499,173 +2523,175 @@ "text": [ "| | name | auroc |\n", "|----:|:---------------------------------------------------------|---------:|\n", - "| 0 | acts-self_attn middle_max | 0.765686 |\n", - "| 1 | acts-self_attn magnitude_filtered_max | 0.752941 |\n", - "| 2 | acts-self_attn first | 0.720588 |\n", - "| 153 | supressed_hs magnitude_filtered_post_softmax_sum 0 | 0.708824 |\n", - "| 3 | acts-self_attn filtered_max | 0.703922 |\n", - "| 4 | acts-self_attn magnitude_filtered(-4) max | 0.702941 |\n", - "| 5 | acts-self_attn doubly_filtered_max | 0.7 |\n", - "| 7 | acts-self_attn percentile_filtered_sum | 0.69902 |\n", - "| 6 | acts-self_attn filtered_mean | 0.69902 |\n", - "| 8 | acts-self_attn max | 0.693137 |\n", - "| 9 | acts-self_attn magnitude_filtered(2) max | 0.692157 |\n", - "| 140 | logits | 0.687255 |\n", - "| 10 | acts-self_attn magnitude_filtered(-8) max | 0.686275 |\n", - "| 11 | acts-self_attn mean | 0.685294 |\n", - "| 12 | hidden_states magnitude_filtered_max | 0.685294 |\n", - "| 13 | acts-self_attn percentile_filtered_mean | 0.685294 |\n", - "| 14 | acts-self_attn doubly_filtered_mean | 0.684314 |\n", - "| 15 | acts-self_attn magnitude_filtered_post_softmax_mean | 0.683333 |\n", - "| 16 | acts-mlp.up_proj magnitude_filtered(0) max | 0.682353 |\n", - "| 17 | acts-self_attn magnitude_filtered_post_softmax_sum | 0.680392 |\n", - "| 18 | acts-self_attn last | 0.67451 |\n", - "| 19 | hidden_states magnitude_filtered(-2) max | 0.67451 |\n", - "| 20 | acts-self_attn magnitude_filtered(0) max | 0.67451 |\n", - "| 21 | acts-mlp.down_proj magnitude_filtered_max | 0.672549 |\n", - "| 161 | supressed_hs magnitude_filtered_post_softmax_sum 0.5 | 0.671569 |\n", - "| 22 | acts-self_attn magnitude_filtered(8) max | 0.671569 |\n", - "| 23 | acts-self_attn none | 0.669608 |\n", - "| 24 | hidden_states magnitude_filtered_post_softmax_sum | 0.669608 |\n", - "| 25 | hidden_states magnitude_filtered_post_softmax_mean | 0.666667 |\n", - "| 26 | acts-mlp.down_proj magnitude_filtered(2) max | 0.666667 |\n", - "| 27 | acts-self_attn magnitude_filtered(4) max | 0.666667 |\n", - "| 28 | acts-self_attn percentile_filtered_max | 0.665686 |\n", - "| 29 | acts-mlp.down_proj magnitude_filtered(-4) max | 0.664706 |\n", - "| 30 | acts-mlp.down_proj magnitude_filtered(-2) max | 0.663725 |\n", - "| 31 | acts-mlp.down_proj filtered_mean | 0.662745 |\n", - "| 32 | hidden_states magnitude_filtered(2) max | 0.661765 |\n", - "| 33 | acts-mlp.down_proj magnitude_filtered(-8) max | 0.661765 |\n", - "| 34 | hidden_states percentile_filtered_sum | 0.659804 |\n", - "| 35 | acts-self_attn entropy_filtered_max | 0.658824 |\n", - "| 36 | acts-mlp.up_proj none | 0.658824 |\n", - "| 37 | acts-self_attn filtered_sum | 0.658824 |\n", - "| 38 | acts-mlp.down_proj filtered_sum | 0.656863 |\n", - "| 39 | hidden_states middle_mean | 0.655882 |\n", - "| 41 | acts-mlp.up_proj percentile_filtered_sum | 0.655882 |\n", - "| 40 | hidden_states filtered_max | 0.655882 |\n", - "| 42 | acts-mlp.up_proj sum | 0.654902 |\n", - "| 43 | hidden_states mean | 0.654902 |\n", - "| 44 | acts-self_attn sum | 0.654902 |\n", - "| 47 | acts-mlp.up_proj magnitude_filtered_post_softmax_mean | 0.652941 |\n", - "| 45 | acts-self_attn middle_sum | 0.652941 |\n", - "| 46 | hidden_states percentile_filtered_mean | 0.652941 |\n", - "| 49 | acts-mlp.down_proj filtered_max | 0.65098 |\n", - "| 48 | acts-mlp.down_proj first | 0.65098 |\n", - "| 50 | acts-self_attn entropy_filtered_sum | 0.65098 |\n", - "| 51 | acts-mlp.up_proj middle_max | 0.65 |\n", - "| 149 | supressed_hs magnitude_filtered_post_softmax_sum -0.1 | 0.65 |\n", - "| 52 | acts-self_attn entropy_filtered_mean | 0.65 |\n", - "| 53 | acts-mlp.down_proj middle_max | 0.64902 |\n", - "| 54 | acts-mlp.down_proj entropy_filtered_max | 0.648039 |\n", - "| 55 | acts-mlp.down_proj magnitude_filtered(4) max | 0.648039 |\n", - "| 56 | acts-self_attn middle_mean | 0.647059 |\n", - "| 57 | hidden_states magnitude_filtered(-1) max | 0.647059 |\n", - "| 58 | acts-self_attn magnitude_filtered_post_softmax_max | 0.646078 |\n", - "| 59 | acts-mlp.down_proj percentile_filtered_max | 0.646078 |\n", - "| 60 | acts-self_attn magnitude_filtered(-2) max | 0.644118 |\n", - "| 61 | hidden_states magnitude_filtered(-4) max | 0.644118 |\n", - "| 62 | hidden_states filtered_mean | 0.643137 |\n", - "| 63 | hidden_states magnitude_filtered(4) max | 0.643137 |\n", - "| 64 | hidden_states magnitude_filtered(8) max | 0.643137 |\n", - "| 65 | acts-mlp.down_proj magnitude_filtered(1) max | 0.642157 |\n", - "| 66 | acts-mlp.down_proj middle_mean | 0.642157 |\n", - "| 67 | acts-mlp.down_proj doubly_filtered_max | 0.642157 |\n", - "| 68 | acts-mlp.down_proj percentile_filtered_mean | 0.642157 |\n", - "| 69 | acts-mlp.down_proj sum | 0.641176 |\n", - "| 70 | acts-mlp.up_proj doubly_filtered_mean | 0.641176 |\n", - "| 71 | acts-mlp.down_proj middle_sum | 0.640196 |\n", - "| 72 | hidden_states entropy_filtered_sum | 0.640196 |\n", - "| 73 | acts-mlp.down_proj magnitude_filtered_post_softmax_mean | 0.640196 |\n", - "| 74 | acts-mlp.down_proj mean | 0.639216 |\n", - "| 75 | acts-mlp.down_proj magnitude_filtered(-1) max | 0.639216 |\n", - "| 76 | hidden_states max | 0.637255 |\n", - "| 77 | acts-mlp.down_proj max | 0.636275 |\n", - "| 78 | acts-mlp.down_proj none | 0.635294 |\n", - "| 79 | hidden_states percentile_filtered_max | 0.635294 |\n", - "| 80 | acts-mlp.down_proj magnitude_filtered_post_softmax_max | 0.635294 |\n", - "| 81 | acts-mlp.down_proj magnitude_filtered(8) max | 0.635294 |\n", - "| 82 | acts-mlp.up_proj filtered_mean | 0.633333 |\n", - "| 83 | hidden_states magnitude_filtered_sum | 0.632353 |\n", - "| 84 | hidden_states magnitude_filtered_post_softmax_max | 0.632353 |\n", - "| 85 | hidden_states middle_max | 0.631373 |\n", - "| 86 | hidden_states magnitude_filtered(-8) max | 0.631373 |\n", - "| 159 | supressed_hs magnitude_filtered_post_softmax_sum 0.1 | 0.631373 |\n", - "| 87 | hidden_states entropy_filtered_max | 0.630392 |\n", - "| 88 | acts-mlp.up_proj percentile_filtered_max | 0.630392 |\n", - "| 89 | hidden_states sum | 0.629412 |\n", - "| 155 | supressed_hs magnitude_filtered_post_softmax_sum 0 | 0.629412 |\n", - "| 90 | hidden_states middle_sum | 0.629412 |\n", - "| 91 | acts-mlp.up_proj max | 0.628431 |\n", - "| 92 | acts-self_attn magnitude_filtered_mean | 0.628431 |\n", - "| 93 | hidden_states none | 0.626471 |\n", - "| 94 | hidden_states last | 0.62549 |\n", - "| 95 | acts-mlp.up_proj magnitude_filtered_post_softmax_sum | 0.62549 |\n", - "| 96 | hidden_states magnitude_filtered_mean | 0.62451 |\n", - "| 98 | acts-mlp.up_proj magnitude_filtered(2) max | 0.62451 |\n", - "| 97 | acts-mlp.down_proj magnitude_filtered_mean | 0.62451 |\n", - "| 99 | acts-mlp.up_proj magnitude_filtered(-2) max | 0.623529 |\n", - "| 100 | acts-mlp.up_proj middle_sum | 0.622549 |\n", - "| 101 | acts-mlp.down_proj entropy_filtered_mean | 0.622549 |\n", - "| 102 | acts-mlp.up_proj percentile_filtered_mean | 0.620588 |\n", - "| 103 | acts-mlp.down_proj magnitude_filtered_sum | 0.619608 |\n", - "| 104 | acts-mlp.up_proj entropy_filtered_mean | 0.619608 |\n", - "| 146 | supressed_mask magnitude_filtered_post_softmax_sum -1 | 0.619608 |\n", - "| 105 | acts-mlp.down_proj percentile_filtered_sum | 0.618627 |\n", - "| 106 | acts-mlp.up_proj filtered_sum | 0.617647 |\n", - "| 107 | acts-self_attn magnitude_filtered_sum | 0.615686 |\n", - "| 108 | acts-mlp.up_proj filtered_max | 0.615686 |\n", - "| 109 | hidden_states entropy_filtered_mean | 0.614706 |\n", - "| 110 | acts-mlp.up_proj entropy_filtered_sum | 0.614706 |\n", - "| 111 | acts-mlp.up_proj doubly_filtered_max | 0.613725 |\n", - "| 112 | hidden_states first | 0.613725 |\n", - "| 113 | acts-mlp.down_proj entropy_filtered_sum | 0.612745 |\n", - "| 115 | acts-mlp.up_proj magnitude_filtered_max | 0.611765 |\n", - "| 114 | acts-mlp.up_proj last | 0.611765 |\n", - "| 116 | acts-mlp.up_proj magnitude_filtered(-4) max | 0.610784 |\n", - "| 158 | supressed_mask magnitude_filtered_post_softmax_sum 0.01 | 0.609804 |\n", - "| 117 | acts-mlp.up_proj magnitude_filtered(-1) max | 0.604902 |\n", - "| 118 | acts-mlp.up_proj magnitude_filtered(4) max | 0.604902 |\n", - "| 150 | supressed_mask magnitude_filtered_post_softmax_sum -0.1 | 0.603922 |\n", - "| 162 | supressed_mask magnitude_filtered_post_softmax_sum 0.5 | 0.603922 |\n", - "| 119 | acts-mlp.up_proj magnitude_filtered_sum | 0.602941 |\n", - "| 120 | acts-mlp.down_proj doubly_filtered_mean | 0.60098 |\n", - "| 121 | acts-mlp.up_proj magnitude_filtered_mean | 0.60098 |\n", - "| 122 | hidden_states doubly_filtered_max | 0.60098 |\n", - "| 123 | hidden_states magnitude_filtered(1) max | 0.598039 |\n", - "| 124 | acts-mlp.up_proj mean | 0.597059 |\n", - "| 126 | acts-self_attn magnitude_filtered(1) max | 0.597059 |\n", - "| 125 | acts-mlp.up_proj entropy_filtered_max | 0.597059 |\n", - "| 127 | acts-mlp.down_proj magnitude_filtered_post_softmax_sum | 0.595098 |\n", - "| 128 | acts-mlp.down_proj last | 0.594118 |\n", - "| 148 | supressed_mask magnitude_filtered_post_softmax_sum -0.5 | 0.589216 |\n", - "| 129 | acts-mlp.up_proj middle_mean | 0.588235 |\n", - "| 130 | acts-mlp.up_proj magnitude_filtered(1) max | 0.586275 |\n", - "| 165 | supressed_hs magnitude_filtered_post_softmax_sum 10 | 0.583824 |\n", - "| 132 | acts-mlp.down_proj magnitude_filtered(0) max | 0.583333 |\n", - "| 131 | acts-mlp.up_proj magnitude_filtered_post_softmax_max | 0.583333 |\n", - "| 157 | supressed_hs magnitude_filtered_post_softmax_sum 0.01 | 0.581373 |\n", - "| 151 | supressed_hs magnitude_filtered_post_softmax_sum -0.01 | 0.579412 |\n", - "| 133 | acts-mlp.up_proj first | 0.57451 |\n", - "| 134 | hidden_states doubly_filtered_mean | 0.57451 |\n", - "| 135 | acts-mlp.up_proj magnitude_filtered(8) max | 0.57451 |\n", - "| 163 | supressed_hs magnitude_filtered_post_softmax_sum 1 | 0.569608 |\n", - "| 136 | hidden_states magnitude_filtered(0) max | 0.559804 |\n", - "| 147 | supressed_hs magnitude_filtered_post_softmax_sum -0.5 | 0.552941 |\n", - "| 145 | supressed_hs magnitude_filtered_post_softmax_sum -1 | 0.54902 |\n", - "| 137 | hidden_states filtered_sum | 0.547059 |\n", - "| 141 | llm_ans | 0.543137 |\n", - "| 138 | acts-self_attn magnitude_filtered(-1) max | 0.540196 |\n", - "| 160 | supressed_mask magnitude_filtered_post_softmax_sum 0.1 | 0.539216 |\n", - "| 154 | supressed_mask magnitude_filtered_post_softmax_sum 0 | 0.536275 |\n", - "| 139 | acts-mlp.up_proj magnitude_filtered(-8) max | 0.529412 |\n", - "| 144 | supressed_mask magnitude_filtered_post_softmax_sum -5 | 0.519608 |\n", - "| 143 | supressed_hs magnitude_filtered_post_softmax_sum -5 | 0.514216 |\n", - "| 152 | supressed_mask magnitude_filtered_post_softmax_sum -0.01 | 0.508824 |\n", - "| 166 | supressed_mask magnitude_filtered_post_softmax_sum 10 | 0.503922 |\n", - "| 142 | llm_log_prob_true | 0.498529 |\n", - "| 156 | supressed_mask magnitude_filtered_post_softmax_sum 0 | 0.460784 |\n", - "| 164 | supressed_mask magnitude_filtered_post_softmax_sum 1 | 0.459804 |\n" + "| 0 | acts-mlp.up_proj last | 0.716667 |\n", + "| 1 | acts-self_attn middle_sum | 0.683333 |\n", + "| 2 | acts-self_attn magnitude_filtered_post_softmax_max | 0.680392 |\n", + "| 3 | acts-mlp.up_proj magnitude_filtered(8) max | 0.67549 |\n", + "| 4 | acts-self_attn percentile_filtered_mean | 0.673529 |\n", + "| 5 | acts-mlp.down_proj entropy_filtered_mean | 0.672549 |\n", + "| 6 | hidden_states magnitude_filtered(-1) max | 0.665686 |\n", + "| 154 | supressed_mask magnitude_filtered_post_softmax_sum -0.01 | 0.659804 |\n", + "| 7 | acts-self_attn magnitude_filtered_post_softmax_mean | 0.655882 |\n", + "| 8 | acts-self_attn middle_mean | 0.654902 |\n", + "| 9 | acts-self_attn magnitude_filtered_post_softmax_sum | 0.65 |\n", + "| 10 | acts-self_attn filtered_mean | 0.645098 |\n", + "| 11 | acts-self_attn percentile_filtered_sum | 0.643137 |\n", + "| 142 | llm_log_prob_true | 0.642157 |\n", + "| 12 | acts-self_attn percentile_filtered_max | 0.637255 |\n", + "| 13 | acts-self_attn magnitude_filtered_mean | 0.636275 |\n", + "| 14 | acts-mlp.up_proj magnitude_filtered(-4) max | 0.631373 |\n", + "| 15 | acts-self_attn magnitude_filtered(-8) max | 0.629412 |\n", + "| 16 | acts-self_attn filtered_sum | 0.626471 |\n", + "| 17 | acts-mlp.up_proj filtered_sum | 0.62549 |\n", + "| 18 | acts-mlp.down_proj middle_sum | 0.62451 |\n", + "| 19 | hidden_states first | 0.623529 |\n", + "| 20 | acts-mlp.down_proj magnitude_filtered(-2) max | 0.620588 |\n", + "| 21 | acts-mlp.up_proj magnitude_filtered(1) max | 0.618627 |\n", + "| 22 | acts-mlp.up_proj filtered_max | 0.617647 |\n", + "| 148 | supressed_mask magnitude_filtered_post_softmax_sum -1 | 0.609804 |\n", + "| 23 | acts-mlp.down_proj magnitude_filtered_post_softmax_sum | 0.608824 |\n", + "| 25 | acts-self_attn magnitude_filtered_sum | 0.606863 |\n", + "| 24 | acts-self_attn mean | 0.606863 |\n", + "| 26 | acts-mlp.up_proj entropy_filtered_mean | 0.606863 |\n", + "| 27 | hidden_states middle_mean | 0.603922 |\n", + "| 28 | acts-mlp.down_proj max | 0.602941 |\n", + "| 29 | acts-mlp.up_proj percentile_filtered_mean | 0.601961 |\n", + "| 153 | supressed_hs magnitude_filtered_post_softmax_sum -0.01 | 0.598039 |\n", + "| 30 | acts-mlp.down_proj none | 0.596078 |\n", + "| 31 | acts-mlp.up_proj middle_mean | 0.596078 |\n", + "| 32 | acts-self_attn doubly_filtered_mean | 0.596078 |\n", + "| 33 | acts-mlp.up_proj doubly_filtered_mean | 0.594118 |\n", + "| 34 | acts-self_attn first | 0.593137 |\n", + "| 35 | acts-mlp.down_proj entropy_filtered_max | 0.592157 |\n", + "| 36 | acts-mlp.up_proj entropy_filtered_sum | 0.592157 |\n", + "| 38 | acts-mlp.down_proj sum | 0.591176 |\n", + "| 37 | acts-mlp.down_proj mean | 0.591176 |\n", + "| 39 | acts-self_attn magnitude_filtered(4) max | 0.590196 |\n", + "| 40 | acts-mlp.down_proj filtered_mean | 0.590196 |\n", + "| 41 | acts-mlp.up_proj sum | 0.587255 |\n", + "| 42 | acts-mlp.down_proj magnitude_filtered(1) max | 0.586275 |\n", + "| 43 | acts-mlp.down_proj magnitude_filtered_post_softmax_max | 0.585294 |\n", + "| 44 | acts-mlp.down_proj entropy_filtered_sum | 0.584314 |\n", + "| 45 | acts-mlp.down_proj middle_mean | 0.582353 |\n", + "| 155 | supressed_hs magnitude_filtered_post_softmax_sum 0 | 0.581373 |\n", + "| 46 | acts-mlp.up_proj magnitude_filtered_mean | 0.581373 |\n", + "| 47 | acts-self_attn entropy_filtered_mean | 0.580392 |\n", + "| 48 | acts-mlp.up_proj magnitude_filtered(-2) max | 0.579412 |\n", + "| 49 | acts-mlp.up_proj max | 0.578431 |\n", + "| 50 | acts-mlp.down_proj doubly_filtered_mean | 0.578431 |\n", + "| 51 | acts-mlp.up_proj mean | 0.578431 |\n", + "| 52 | acts-self_attn filtered_max | 0.578431 |\n", + "| 53 | hidden_states entropy_filtered_max | 0.576471 |\n", + "| 54 | acts-mlp.down_proj last | 0.57549 |\n", + "| 55 | acts-mlp.down_proj percentile_filtered_mean | 0.571569 |\n", + "| 56 | hidden_states middle_sum | 0.570588 |\n", + "| 152 | supressed_mask magnitude_filtered_post_softmax_sum -0.1 | 0.569608 |\n", + "| 57 | acts-mlp.up_proj magnitude_filtered_post_softmax_mean | 0.569608 |\n", + "| 58 | acts-mlp.up_proj magnitude_filtered_post_softmax_max | 0.569608 |\n", + "| 59 | acts-self_attn magnitude_filtered(-2) max | 0.569608 |\n", + "| 60 | acts-mlp.up_proj middle_max | 0.566667 |\n", + "| 61 | acts-mlp.down_proj magnitude_filtered(8) max | 0.566667 |\n", + "| 62 | hidden_states percentile_filtered_sum | 0.565686 |\n", + "| 63 | acts-mlp.down_proj magnitude_filtered(2) max | 0.565686 |\n", + "| 64 | hidden_states sum | 0.564706 |\n", + "| 65 | acts-self_attn magnitude_filtered_max | 0.561765 |\n", + "| 66 | acts-mlp.down_proj magnitude_filtered(-4) max | 0.561765 |\n", + "| 69 | acts-self_attn magnitude_filtered(1) max | 0.560784 |\n", + "| 68 | acts-mlp.down_proj filtered_sum | 0.560784 |\n", + "| 67 | acts-self_attn sum | 0.560784 |\n", + "| 70 | hidden_states entropy_filtered_sum | 0.559804 |\n", + "| 71 | acts-mlp.up_proj middle_sum | 0.558824 |\n", + "| 72 | acts-self_attn magnitude_filtered(8) max | 0.558824 |\n", + "| 73 | hidden_states entropy_filtered_mean | 0.557843 |\n", + "| 74 | acts-self_attn middle_max | 0.556863 |\n", + "| 75 | acts-self_attn none | 0.555882 |\n", + "| 76 | acts-mlp.up_proj entropy_filtered_max | 0.555882 |\n", + "| 158 | supressed_mask magnitude_filtered_post_softmax_sum 0 | 0.552941 |\n", + "| 77 | acts-mlp.down_proj filtered_max | 0.551961 |\n", + "| 79 | hidden_states magnitude_filtered(-8) max | 0.551961 |\n", + "| 78 | hidden_states magnitude_filtered_post_softmax_mean | 0.551961 |\n", + "| 80 | hidden_states magnitude_filtered(8) max | 0.551961 |\n", + "| 81 | acts-mlp.up_proj magnitude_filtered_sum | 0.55098 |\n", + "| 85 | acts-mlp.down_proj magnitude_filtered(0) max | 0.55 |\n", + "| 82 | hidden_states filtered_sum | 0.55 |\n", + "| 83 | acts-mlp.down_proj magnitude_filtered_sum | 0.55 |\n", + "| 84 | acts-mlp.up_proj magnitude_filtered(-8) max | 0.55 |\n", + "| 86 | acts-self_attn magnitude_filtered(2) max | 0.55 |\n", + "| 87 | acts-mlp.down_proj magnitude_filtered_mean | 0.55 |\n", + "| 88 | acts-mlp.up_proj first | 0.546078 |\n", + "| 89 | hidden_states magnitude_filtered(-4) max | 0.544118 |\n", + "| 90 | hidden_states magnitude_filtered(2) max | 0.544118 |\n", + "| 91 | acts-self_attn last | 0.544118 |\n", + "| 92 | acts-mlp.down_proj magnitude_filtered_post_softmax_mean | 0.538235 |\n", + "| 93 | acts-self_attn entropy_filtered_sum | 0.537255 |\n", + "| 164 | supressed_mask magnitude_filtered_post_softmax_sum 0.5 | 0.537255 |\n", + "| 94 | hidden_states magnitude_filtered_max | 0.537255 |\n", + "| 141 | llm_ans | 0.536275 |\n", + "| 95 | acts-mlp.up_proj doubly_filtered_max | 0.536275 |\n", + "| 96 | acts-mlp.up_proj magnitude_filtered(2) max | 0.535294 |\n", + "| 97 | hidden_states magnitude_filtered_post_softmax_max | 0.530392 |\n", + "| 98 | hidden_states magnitude_filtered(4) max | 0.530392 |\n", + "| 99 | hidden_states mean | 0.527451 |\n", + "| 100 | hidden_states magnitude_filtered(0) max | 0.527451 |\n", + "| 150 | supressed_mask magnitude_filtered_post_softmax_sum -0.5 | 0.526471 |\n", + "| 102 | acts-mlp.up_proj percentile_filtered_sum | 0.52451 |\n", + "| 101 | acts-mlp.up_proj filtered_mean | 0.52451 |\n", + "| 103 | hidden_states magnitude_filtered(-2) max | 0.52451 |\n", + "| 104 | acts-self_attn entropy_filtered_max | 0.522549 |\n", + "| 105 | acts-mlp.down_proj percentile_filtered_sum | 0.521569 |\n", + "| 160 | supressed_mask magnitude_filtered_post_softmax_sum 0.01 | 0.521569 |\n", + "| 106 | acts-self_attn max | 0.520588 |\n", + "| 107 | acts-self_attn doubly_filtered_max | 0.519608 |\n", + "| 108 | acts-mlp.up_proj magnitude_filtered(-1) max | 0.519608 |\n", + "| 109 | hidden_states percentile_filtered_mean | 0.518627 |\n", + "| 161 | supressed_hs magnitude_filtered_post_softmax_sum 0.1 | 0.515686 |\n", + "| 110 | acts-self_attn magnitude_filtered(-1) max | 0.515686 |\n", + "| 111 | acts-mlp.up_proj magnitude_filtered_post_softmax_sum | 0.511765 |\n", + "| 112 | acts-mlp.down_proj magnitude_filtered(4) max | 0.506863 |\n", + "| 113 | acts-mlp.up_proj magnitude_filtered(4) max | 0.505882 |\n", + "| 114 | acts-mlp.down_proj magnitude_filtered(-8) max | 0.504902 |\n", + "| 115 | hidden_states max | 0.502941 |\n", + "| 116 | hidden_states doubly_filtered_mean | 0.502941 |\n", + "| 117 | acts-mlp.up_proj percentile_filtered_max | 0.502941 |\n", + "| 118 | hidden_states doubly_filtered_max | 0.50098 |\n", + "| 159 | supressed_hs magnitude_filtered_post_softmax_sum 0.01 | 0.50098 |\n", + "| 157 | supressed_hs magnitude_filtered_post_softmax_sum 0 | 0.50098 |\n", + "| 119 | acts-mlp.down_proj doubly_filtered_max | 0.50098 |\n", + "| 120 | hidden_states percentile_filtered_max | 0.5 |\n", + "| 121 | acts-mlp.down_proj percentile_filtered_max | 0.5 |\n", + "| 163 | supressed_hs magnitude_filtered_post_softmax_sum 0.5 | 0.498039 |\n", + "| 122 | acts-mlp.up_proj none | 0.495098 |\n", + "| 143 | supressed_hs magnitude_filtered_post_softmax_sum -10 | 0.495098 |\n", + "| 124 | hidden_states magnitude_filtered(1) max | 0.494118 |\n", + "| 123 | acts-self_attn magnitude_filtered(-4) max | 0.494118 |\n", + "| 125 | hidden_states middle_max | 0.491176 |\n", + "| 147 | supressed_hs magnitude_filtered_post_softmax_sum -1 | 0.490196 |\n", + "| 126 | hidden_states magnitude_filtered_post_softmax_sum | 0.485294 |\n", + "| 140 | logits | 0.485294 |\n", + "| 127 | hidden_states magnitude_filtered_sum | 0.485294 |\n", + "| 167 | supressed_hs magnitude_filtered_post_softmax_sum 10 | 0.484314 |\n", + "| 128 | acts-mlp.up_proj magnitude_filtered(0) max | 0.478431 |\n", + "| 129 | hidden_states last | 0.477451 |\n", + "| 156 | supressed_mask magnitude_filtered_post_softmax_sum 0 | 0.471569 |\n", + "| 130 | hidden_states magnitude_filtered_mean | 0.469608 |\n", + "| 131 | acts-self_attn magnitude_filtered(0) max | 0.469608 |\n", + "| 132 | hidden_states filtered_mean | 0.466667 |\n", + "| 133 | acts-mlp.up_proj magnitude_filtered_max | 0.464706 |\n", + "| 168 | supressed_mask magnitude_filtered_post_softmax_sum 10 | 0.463725 |\n", + "| 134 | acts-mlp.down_proj magnitude_filtered_max | 0.462745 |\n", + "| 145 | supressed_hs magnitude_filtered_post_softmax_sum -5 | 0.460784 |\n", + "| 135 | hidden_states filtered_max | 0.457843 |\n", + "| 146 | supressed_mask magnitude_filtered_post_softmax_sum -5 | 0.451961 |\n", + "| 166 | supressed_mask magnitude_filtered_post_softmax_sum 1 | 0.45098 |\n", + "| 136 | acts-mlp.down_proj middle_max | 0.44902 |\n", + "| 151 | supressed_hs magnitude_filtered_post_softmax_sum -0.1 | 0.442157 |\n", + "| 162 | supressed_mask magnitude_filtered_post_softmax_sum 0.1 | 0.435294 |\n", + "| 144 | supressed_mask magnitude_filtered_post_softmax_sum -10 | 0.422059 |\n", + "| 165 | supressed_hs magnitude_filtered_post_softmax_sum 1 | 0.414706 |\n", + "| 137 | hidden_states none | 0.403922 |\n", + "| 138 | acts-mlp.down_proj first | 0.391176 |\n", + "| 139 | acts-mlp.down_proj magnitude_filtered(-1) max | 0.391176 |\n", + "| 149 | supressed_hs magnitude_filtered_post_softmax_sum -0.5 | 0.369608 |\n" ] } ], @@ -2716,29 +2742,14 @@ " \n", " \n", " \n", - " acts-self_attn\n", - " acts-self_attn sum\n", - " 0.765686\n", - " \n", - " \n", - " supressed_hs\n", - " supressed_hs magnitude_filtered_post_softmax_s...\n", - " 0.708824\n", - " \n", - " \n", - " logits\n", - " logits\n", - " 0.687255\n", - " \n", - " \n", - " hidden_states\n", - " hidden_states sum\n", - " 0.685294\n", - " \n", - " \n", " acts-mlp.up_proj\n", " acts-mlp.up_proj sum\n", - " 0.682353\n", + " 0.716667\n", + " \n", + " \n", + " acts-self_attn\n", + " acts-self_attn sum\n", + " 0.683333\n", " \n", " \n", " acts-mlp.down_proj\n", @@ -2746,19 +2757,34 @@ " 0.672549\n", " \n", " \n", - " supressed_mask\n", - " supressed_mask magnitude_filtered_post_softmax...\n", - " 0.619608\n", + " hidden_states\n", + " hidden_states sum\n", + " 0.665686\n", " \n", " \n", - " llm_ans\n", - " llm_ans\n", - " 0.543137\n", + " supressed_mask\n", + " supressed_mask magnitude_filtered_post_softmax...\n", + " 0.659804\n", " \n", " \n", " llm_log_prob_true\n", " llm_log_prob_true\n", - " 0.498529\n", + " 0.642157\n", + " \n", + " \n", + " supressed_hs\n", + " supressed_hs magnitude_filtered_post_softmax_s...\n", + " 0.598039\n", + " \n", + " \n", + " llm_ans\n", + " llm_ans\n", + " 0.536275\n", + " \n", + " \n", + " logits\n", + " logits\n", + " 0.485294\n", " \n", " \n", "\n", @@ -2767,27 +2793,27 @@ "text/plain": [ " name \\\n", "data \n", - "acts-self_attn acts-self_attn sum \n", - "supressed_hs supressed_hs magnitude_filtered_post_softmax_s... \n", - "logits logits \n", - "hidden_states hidden_states sum \n", "acts-mlp.up_proj acts-mlp.up_proj sum \n", + "acts-self_attn acts-self_attn sum \n", "acts-mlp.down_proj acts-mlp.down_proj sum \n", + "hidden_states hidden_states sum \n", "supressed_mask supressed_mask magnitude_filtered_post_softmax... \n", - "llm_ans llm_ans \n", "llm_log_prob_true llm_log_prob_true \n", + "supressed_hs supressed_hs magnitude_filtered_post_softmax_s... \n", + "llm_ans llm_ans \n", + "logits logits \n", "\n", " auroc \n", "data \n", - "acts-self_attn 0.765686 \n", - "supressed_hs 0.708824 \n", - "logits 0.687255 \n", - "hidden_states 0.685294 \n", - "acts-mlp.up_proj 0.682353 \n", + "acts-mlp.up_proj 0.716667 \n", + "acts-self_attn 0.683333 \n", "acts-mlp.down_proj 0.672549 \n", - "supressed_mask 0.619608 \n", - "llm_ans 0.543137 \n", - "llm_log_prob_true 0.498529 " + "hidden_states 0.665686 \n", + "supressed_mask 0.659804 \n", + "llm_log_prob_true 0.642157 \n", + "supressed_hs 0.598039 \n", + "llm_ans 0.536275 \n", + "logits 0.485294 " ] }, "execution_count": 38, @@ -2810,14 +2836,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_2637475/1082231495.py:19: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "/tmp/ipykernel_2758990/1082231495.py:19: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", " plt.legend().remove()\n" ] }, { "data": { "text/plain": [ - "PosixPath('../figs/truthfulqa_Qwen_Qwen2.5-0.5B-Instruct.png')" + "PosixPath('../figs/truthfulqa_Qwen_Qwen3-4B.png')" ] }, "execution_count": 39, @@ -2826,7 +2852,7 @@ }, { "data": { - "image/png": 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", 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", 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