diff --git a/activation_store/collect.py b/activation_store/collect.py
index d6ee003..509de17 100644
--- a/activation_store/collect.py
+++ b/activation_store/collect.py
@@ -10,29 +10,33 @@ from einops import rearrange
 from datasets.arrow_writer import ArrowWriter, ParquetWriter
 from datasets.fingerprint import Hasher
 from transformers.modeling_outputs import ModelOutput
-
 from activation_store.helpers.torch import clear_mem
 from typing import Dict, Generator
 from torch import Tensor
 
 default_output_folder = (Path(__file__).parent.parent / "outputs").resolve()
 
-def default_postprocess_result(input: dict, trace: TraceDict, output: ModelOutput, model: AutoModelForCausalLM) -> Dict[str, Tensor]:
+@torch.no_grad()
+def default_postprocess_result(input: dict, trace: TraceDict, output: ModelOutput, model: AutoModelForCausalLM, last_token=False) -> Dict[str, Tensor]:
     """Make your own. This adds activations to output, and rearranges hidden states"""
+    token_index = -1 if last_token else slice(None)
 
     # Baukit records the literal layer output, which varies by model. Here we assume that the output or the first part are activations we want
     acts = {f'act-{k}': 
-            v.output[0] if isinstance(v.output, tuple) else v.output
+            v.output[0][:, token_index] if isinstance(v.output, tuple) else v.output
             for k, v in trace.items()}
+    del trace
     
     # batch must be first, also the writer supports float16 so lets use that
-    output.hidden_states = rearrange(list(output.hidden_states), 'l b t h -> b l t h').half()
+    output.hidden_states = rearrange(list(output.hidden_states), 'l b t h -> b l t h')[:, token_index].half()
 
     o = dict(**acts, **output)
-    if 'attention_mask' in input:
+    if ('attention_mask' in input) and not last_token:
         o['attention_mask'] = input['attention_mask']
     if 'label' in input:
         o['label'] = input['label']
+    input = output = acts = None
+
     return o
 
 
@@ -41,8 +45,11 @@ def generate_batches(loader: DataLoader, model: AutoModelForCausalLM, layers, po
     model.eval()
     for batch in tqdm(loader, 'collecting activations'):
         device = next(model.parameters()).device
-        with torch.amp.autocast(device_type=device.type):
-            with TraceDict(model, layers) as trace:
+        with torch.autocast(device_type=device.type):
+
+            # FIXME for some reason autocast isn't converting the inputs
+            batch = {k: v.to(device) if isinstance(v, Tensor) else v for k, v in batch.items()}
+            with TraceDict(model, layers, retain_grad=False, detach=True, clone=True) as trace:
                 out = model(**batch, use_cache=False, output_hidden_states=True, return_dict=True)
         o = postprocess_result(batch, trace, out, model)