diff --git a/mjc_notes.md b/mjc_notes.md
index 4809c93..549be66 100644
--- a/mjc_notes.md
+++ b/mjc_notes.md
@@ -1821,4 +1821,7 @@ Ok what should my pipeline do?
 
 
 TODO:
-- [ ] change layer -1 to actual name?
+- [x] change layer -1 to actual name?
+- [ ] in the TQS notebook they only get the mean from the generated tokens
+- [ ] they only interven on the ones that are not padded... wait no they passed in no token pos in honesty
+- [ ] but they did in TQA
diff --git a/notebooks/201_make_data.ipynb b/notebooks/201_make_data.ipynb
index aa8f757..ffe7c26 100644
--- a/notebooks/201_make_data.ipynb
+++ b/notebooks/201_make_data.ipynb
@@ -87,12 +87,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\u001b[32m2023-10-24 10:49:20.938\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mchanging pad_token_id from 32000 to 0\u001b[0m\n",
-      "2023-10-24T10:49:20.938690+0800 INFO changing pad_token_id from 32000 to 0\n",
-      "\u001b[32m2023-10-24 10:49:20.940\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mchanging padding_side from right to left\u001b[0m\n",
-      "2023-10-24T10:49:20.940322+0800 INFO changing padding_side from right to left\n",
-      "\u001b[32m2023-10-24 10:49:20.941\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mchanging truncation_side from right to left\u001b[0m\n",
-      "2023-10-24T10:49:20.941383+0800 INFO changing truncation_side from right to left\n"
+      "\u001b[32m2023-10-24 12:20:50.811\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mchanging pad_token_id from 32000 to 0\u001b[0m\n",
+      "2023-10-24T12:20:50.811435+0800 INFO changing pad_token_id from 32000 to 0\n",
+      "\u001b[32m2023-10-24 12:20:50.812\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mchanging padding_side from right to left\u001b[0m\n",
+      "2023-10-24T12:20:50.812537+0800 INFO changing padding_side from right to left\n",
+      "\u001b[32m2023-10-24 12:20:50.813\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36msrc.models.load\u001b[0m:\u001b[36mverbose_change_param\u001b[0m:\u001b[36m18\u001b[0m - \u001b[1mchanging truncation_side from right to left\u001b[0m\n",
+      "2023-10-24T12:20:50.813021+0800 INFO changing truncation_side from right to left\n"
      ]
     }
    ],
@@ -147,11 +147,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "tokenize: 100%|██████████| 302/302 [00:00<00:00, 3490.12 examples/s]\n",
-      "truncated: 100%|██████████| 302/302 [00:00<00:00, 3889.90 examples/s]\n",
-      "prompt_truncated: 100%|██████████| 302/302 [00:00<00:00, 604.08 examples/s]\n",
-      "choice_ids: 100%|██████████| 302/302 [00:00<00:00, 9749.77 examples/s]\n",
-      "Filter: 100%|██████████| 302/302 [00:00<00:00, 3987.01 examples/s]"
+      "tokenize: 100%|██████████| 302/302 [00:00<00:00, 2898.85 examples/s]\n",
+      "truncated: 100%|██████████| 302/302 [00:00<00:00, 3922.12 examples/s]\n",
+      "prompt_truncated: 100%|██████████| 302/302 [00:00<00:00, 565.78 examples/s]\n",
+      "choice_ids: 100%|██████████| 302/302 [00:00<00:00, 10226.05 examples/s]\n",
+      "Filter: 100%|██████████| 302/302 [00:00<00:00, 3935.72 examples/s]"
      ]
     },
     {
@@ -236,7 +236,7 @@
     {
      "data": {
       "text/plain": [
-       "<src.repe.rep_readers.PCARepReader at 0x7fcd97b27250>"
+       "<src.repe.rep_readers.PCARepReader at 0x7ff755ec4ee0>"
       ]
      },
      "execution_count": 8,
@@ -268,17 +268,17 @@
     {
      "data": {
       "text/plain": [
-       "{8: -7.1495447,\n",
-       " 11: 16.796896,\n",
-       " 14: 13.5725565,\n",
-       " 17: -5.684828,\n",
-       " 20: 26.571663,\n",
-       " 23: 37.01544,\n",
-       " 26: -67.76076,\n",
-       " 29: -64.502205,\n",
-       " 32: -52.72602,\n",
-       " 35: -76.70331,\n",
-       " 38: -80.74953}"
+       "{8: 1.661585,\n",
+       " 11: 4.2028227,\n",
+       " 14: -1.7875421,\n",
+       " 17: -6.6559744,\n",
+       " 20: -7.8236346,\n",
+       " 23: -10.668092,\n",
+       " 26: -1.1562811,\n",
+       " 29: -2.477594,\n",
+       " 32: -3.406129,\n",
+       " 35: -6.056723,\n",
+       " 38: 3.0787961}"
       ]
      },
      "execution_count": 9,
@@ -308,61 +308,241 @@
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<src.repe.rep_control_pipeline.RepControlPipeline at 0x7ff755ec4ca0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
-    "# layer_id = hidden_layers\n",
-    "# block_name=\"decoder_block\"\n",
-    "# control_method=\"reading_vec\"\n",
+    "layer_id = hidden_layers\n",
+    "block_name=\"decoder_block\"\n",
+    "control_method=\"reading_vec\"\n",
     "\n",
-    "# rep_control_pipeline = pipeline(\n",
-    "#     \"rep-control\", \n",
-    "#     model=model, \n",
-    "#     tokenizer=tokenizer, \n",
-    "#     layers=layer_id, max_length=cfg.max_length,\n",
-    "#     control_method=control_method)\n",
-    "# rep_control_pipeline\n"
+    "rep_control_pipeline = pipeline(\n",
+    "    \"rep-control\", \n",
+    "    model=model, \n",
+    "    tokenizer=tokenizer, \n",
+    "    layers=layer_id, max_length=cfg.max_length,\n",
+    "    control_method=control_method)\n",
+    "rep_control_pipeline\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===== No Control =====\n",
+      "negative\n",
+      "===== + Honesty Control =====\n",
+      "negative\n",
+      "\n",
+      "===== - Honesty Control =====\n",
+      "I\n",
+      "\n",
+      "===== No Control =====\n",
+      "pos\n",
+      "===== + Honesty Control =====\n",
+      "pos\n",
+      "\n",
+      "===== - Honesty Control =====\n",
+      "pos\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "\n",
-    "# from re import S\n",
+    "from re import S\n",
+    "layer_id = hidden_layers\n",
     "\n",
+    "inputs = dataset_train[:2]\n",
+    "coeff=8.0\n",
+    "max_new_tokens=1\n",
+    "text_gen_kwargs = dict(do_sample=False, max_new_tokens=max_new_tokens, use_cache=False, \n",
+    "                       output_hidden_states=True, return_dict=True,\n",
+    "                       )\n",
     "\n",
-    "# inputs = dataset_train[:2]\n",
-    "# coeff=8.0\n",
-    "# max_new_tokens=1\n",
-    "# text_gen_kwargs = dict(do_sample=False, max_new_tokens=max_new_tokens, use_cache=False, \n",
-    "#                        output_hidden_states=True, return_dict=True,\n",
-    "#                        )\n",
-    "\n",
-    "# activations = {}\n",
-    "# for layer in layer_id:\n",
-    "#     activations[layer] = torch.tensor(coeff * honesty_rep_reader.directions[layer] * honesty_rep_reader.direction_signs[layer]).to(model.device).half()\n",
+    "activations = {}\n",
+    "for layer in layer_id:\n",
+    "    activations[layer] = torch.tensor(coeff * honesty_rep_reader.directions[layer] * honesty_rep_reader.direction_signs[layer]).to(model.device).half()\n",
     "    \n",
     "\n",
-    "# activations_neg = {k:-v for k,v in activations.items()}\n",
+    "activations_neg = {k:-v for k,v in activations.items()}\n",
     "\n",
-    "# model.eval()\n",
-    "# with torch.no_grad():\n",
-    "#     baseline_outputs = rep_control_pipeline(inputs, batch_size=batch_size, **text_gen_kwargs)\n",
-    "#     control_outputs = rep_control_pipeline(inputs, activations=activations, batch_size=batch_size,  **text_gen_kwargs)\n",
-    "#     control_outputs_neg = rep_control_pipeline(inputs, activations=activations_neg, batch_size=batch_size, **text_gen_kwargs)\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    baseline_outputs = rep_control_pipeline(inputs, batch_size=batch_size, **text_gen_kwargs)\n",
+    "    control_outputs = rep_control_pipeline(inputs, activations=activations, batch_size=batch_size,  **text_gen_kwargs)\n",
+    "    control_outputs_neg = rep_control_pipeline(inputs, activations=activations_neg, batch_size=batch_size, **text_gen_kwargs)\n",
     "\n",
-    "# for i,s,p,n in zip(inputs, baseline_outputs['text_ans'], control_outputs['text_ans'], control_outputs_neg['text_ans']):\n",
-    "#     print(\"===== No Control =====\")\n",
-    "#     print(s)\n",
-    "#     print(f\"===== + Honesty Control =====\")\n",
-    "#     print(p)\n",
-    "#     print()\n",
-    "#     print(f\"===== - Honesty Control =====\")\n",
-    "#     print(n)\n",
-    "#     print()\n"
+    "for i,s,p,n in zip(inputs, baseline_outputs['text_ans'], control_outputs['text_ans'], control_outputs_neg['text_ans']):\n",
+    "    print(\"===== No Control =====\")\n",
+    "    print(s)\n",
+    "    print(f\"===== + Honesty Control =====\")\n",
+    "    print(p)\n",
+    "    print()\n",
+    "    print(f\"===== - Honesty Control =====\")\n",
+    "    print(n)\n",
+    "    print()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===== No Control =====\n",
+      "15.20%\n",
+      "===== + Honesty Control =====\n",
+      "19.81%\n",
+      "\n",
+      "===== - Honesty Control =====\n",
+      "29.42%\n",
+      "\n",
+      "===== No Control =====\n",
+      "61.88%\n",
+      "===== + Honesty Control =====\n",
+      "91.96%\n",
+      "\n",
+      "===== - Honesty Control =====\n",
+      "84.79%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i,s,p,n in zip(inputs, baseline_outputs['ans'], control_outputs['ans'], control_outputs_neg['ans']):\n",
+    "    print(\"===== No Control =====\")\n",
+    "    print(f'{s:02.2%}')\n",
+    "    print(f\"===== + Honesty Control =====\")\n",
+    "    print(f'{p:02.2%}')\n",
+    "    print()\n",
+    "    print(f\"===== - Honesty Control =====\")\n",
+    "    print(f'{n:02.2%}')\n",
+    "    print()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===== No Control =====\n",
+      "negative\n",
+      "===== + Honesty Control =====\n",
+      "negative\n",
+      "\n",
+      "===== - Honesty Control =====\n",
+      "I\n",
+      "\n",
+      "===== No Control =====\n",
+      "pos\n",
+      "===== + Honesty Control =====\n",
+      "pos\n",
+      "\n",
+      "===== - Honesty Control =====\n",
+      "pos\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from re import S\n",
+    "layer_id = hidden_layers\n",
+    "\n",
+    "inputs = dataset_train[:2]\n",
+    "coeff=8.0\n",
+    "max_new_tokens=1\n",
+    "text_gen_kwargs = dict(do_sample=False, max_new_tokens=max_new_tokens, use_cache=False, \n",
+    "                       output_hidden_states=True, return_dict=True,\n",
+    "                       token_pos=\"end\",\n",
+    "                       normalize=False\n",
+    "                       )\n",
+    "\n",
+    "activations = {}\n",
+    "for layer in layer_id:\n",
+    "    activations[layer] = torch.tensor(coeff * honesty_rep_reader.directions[layer] * honesty_rep_reader.direction_signs[layer]).to(model.device).half()\n",
+    "    \n",
+    "\n",
+    "activations_neg = {k:-v for k,v in activations.items()}\n",
+    "\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    baseline_outputs = rep_control_pipeline(inputs, batch_size=batch_size, **text_gen_kwargs)\n",
+    "    control_outputs = rep_control_pipeline(inputs, activations=activations, batch_size=batch_size,  **text_gen_kwargs)\n",
+    "    control_outputs_neg = rep_control_pipeline(inputs, activations=activations_neg, batch_size=batch_size, **text_gen_kwargs)\n",
+    "\n",
+    "for i,s,p,n in zip(inputs, baseline_outputs['text_ans'], control_outputs['text_ans'], control_outputs_neg['text_ans']):\n",
+    "    print(\"===== No Control =====\")\n",
+    "    print(s)\n",
+    "    print(f\"===== + Honesty Control =====\")\n",
+    "    print(p)\n",
+    "    print()\n",
+    "    print(f\"===== - Honesty Control =====\")\n",
+    "    print(n)\n",
+    "    print()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "===== No Control =====\n",
+      "15.20%\n",
+      "===== + Honesty Control =====\n",
+      "19.81%\n",
+      "\n",
+      "===== - Honesty Control =====\n",
+      "29.42%\n",
+      "\n",
+      "===== No Control =====\n",
+      "61.88%\n",
+      "===== + Honesty Control =====\n",
+      "91.96%\n",
+      "\n",
+      "===== - Honesty Control =====\n",
+      "84.79%\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i,s,p,n in zip(inputs, baseline_outputs['ans'], control_outputs['ans'], control_outputs_neg['ans']):\n",
+    "    print(\"===== No Control =====\")\n",
+    "    print(f'{s:02.2%}')\n",
+    "    print(f\"===== + Honesty Control =====\")\n",
+    "    print(f'{p:02.2%}')\n",
+    "    print()\n",
+    "    print(f\"===== - Honesty Control =====\")\n",
+    "    print(f'{n:02.2%}')\n",
+    "    print()\n"
    ]
   },
   {
@@ -381,35 +561,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<src.repe.rep_control_pipeline_baukit.RepControlPipeline2 at 0x7fcca5807130>"
+       "<src.repe.rep_control_pipeline_baukit.RepControlPipeline2 at 0x7ff755f5be50>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "layer_id = hidden_layers\n",
     "\n",
-    "rep_control_pipeline = pipeline(\n",
+    "\n",
+    "rep_control_pipeline2 = pipeline(\n",
     "    \"rep-control2\", \n",
     "    model=model, \n",
     "    tokenizer=tokenizer, \n",
     "    layers=layer_id, \n",
     "    max_length=cfg.max_length,)\n",
-    "rep_control_pipeline\n"
+    "rep_control_pipeline2\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -422,15 +602,15 @@
       "negative\n",
       "\n",
       "===== - Honesty Control =====\n",
-      "negative\n",
+      "I\n",
       "\n",
       "===== No Control =====\n",
-      "The\n",
+      "pos\n",
       "===== + Honesty Control =====\n",
-      "The\n",
+      "pos\n",
       "\n",
       "===== - Honesty Control =====\n",
-      "negative\n",
+      "pos\n",
       "\n"
      ]
     }
@@ -452,9 +632,9 @@
     "\n",
     "model.eval()\n",
     "with torch.no_grad():\n",
-    "    baseline_outputs = rep_control_pipeline(inputs, batch_size=batch_size, **text_gen_kwargs)\n",
-    "    control_outputs = rep_control_pipeline(inputs, activations=activations, batch_size=batch_size,  **text_gen_kwargs)\n",
-    "    control_outputs_neg = rep_control_pipeline(inputs, activations=activations_neg, batch_size=batch_size, **text_gen_kwargs)\n",
+    "    baseline_outputs = rep_control_pipeline2(inputs, batch_size=batch_size, **text_gen_kwargs)\n",
+    "    control_outputs = rep_control_pipeline2(inputs, activations=activations, batch_size=batch_size,  **text_gen_kwargs)\n",
+    "    control_outputs_neg = rep_control_pipeline2(inputs, activations=activations_neg, batch_size=batch_size, **text_gen_kwargs)\n",
     "\n",
     "\n",
     "for i,s,p,n in zip(inputs, baseline_outputs['text_ans'], control_outputs['text_ans'], control_outputs_neg['text_ans']):\n",
@@ -470,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -478,20 +658,20 @@
      "output_type": "stream",
      "text": [
       "===== No Control =====\n",
-      "2.03%\n",
+      "15.20%\n",
       "===== + Honesty Control =====\n",
-      "23.10%\n",
+      "19.81%\n",
       "\n",
       "===== - Honesty Control =====\n",
-      "1.02%\n",
+      "29.42%\n",
       "\n",
       "===== No Control =====\n",
-      "33.63%\n",
+      "61.88%\n",
       "===== + Honesty Control =====\n",
-      "83.52%\n",
+      "91.96%\n",
       "\n",
       "===== - Honesty Control =====\n",
-      "7.75%\n",
+      "84.79%\n",
       "\n"
      ]
     }
@@ -508,6 +688,20 @@
     "    print()\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/src/repe/rep_control_pipeline.py b/src/repe/rep_control_pipeline.py
index c89645e..222ddd3 100644
--- a/src/repe/rep_control_pipeline.py
+++ b/src/repe/rep_control_pipeline.py
@@ -35,7 +35,7 @@ class RepControlPipeline(RepControlPipeline2):
         self.layers = layers
         self.max_length = max_length
 
-        super().__init__(model=model, tokenizer=tokenizer, **kwargs)
+        super().__init__(model=model, tokenizer=tokenizer, max_length=max_length, **kwargs)
         
     def __call__(self, text_inputs, activations=None, **kwargs):
         if activations is not None:
diff --git a/src/repe/rep_control_pipeline_baukit.py b/src/repe/rep_control_pipeline_baukit.py
index 3593d33..53ab78b 100644
--- a/src/repe/rep_control_pipeline_baukit.py
+++ b/src/repe/rep_control_pipeline_baukit.py
@@ -20,6 +20,7 @@ def row_choice_ids(answer_choices, tokenizer):
 
 
 def intervene(output, activation):
+    # TODO need attention mask
     assert output.ndim == 3, f"expected output to be (batch, seq, vocab), got {output.shape}"
     return output + activation.to(output.device)[None, None, :]