Formal sents

all.txt

@@ -0,0 +1,33 @@
+Images from television of ten fired her
+Home
+SWEENEY BIG
+WORLD PARTY
+Apart from Fedorov, that fellow who gave us the
+I beat a heavy
+Pleased with the group's healthy sales, Red Rhino allowed the band the luxury of a colour sleeve for their third release, the double A-side, ‘This Boy Can Wait’ and ‘You Should Always Keep In
+Whitbread had no formal education and seems to have been the archetypal self-made
+Their answers result in the supreme irony of lorry-loads of young men, having scraped
+The Society is keeping in close
+In this instance we are again studying a very real struggle, with gangs looting, scavenging and turning to organised crime as a means of scraping
+But Mr Kaifu is in no mood to let anyone steal his
+With me that rings a warning
+Like the constable in the story, use any cover to keep your attacker at
+Organisers of the biggest air rally in Europe say that military red
+Earlier in this chapter ( p. 70 ) striking a realistic
+Inevitably, a book such as this harps
+The bottom line
+Rachel kept in
+It took me breath
+( James Brown, 20/2/88 who made it Single Of The Week along with The Wedding Present's ‘Nobody's Twisting Your
+The achievements are well recorded : the Brabazon airliner, the abortive TSR2 fighter plane and Concorde — the fastest white
+The word is much used by his closest adviser, Hernando de Soto, author of ‘The Other Path’ — a celebrated study of Peru's huge and dense informal sector of street vendors, cottage industries and red
+But it had been years, even then, since Elvis was top of the Hit Parade, and if all he had to offer the world was slicked-back hair and a sulky pout, the Dead
+Not only in Europe, but also in Afghanistan, Africa and East Asia, the Soviet Empire now drew in its
+Why not cut the
+While the credit- driven Anglo economies draw in their
+Old
+I call the
+The writer often called the
+Far from drawing in his
+Fokker is negotiating with Lockheed to assemble aircraft in America : this would bring Lockheed back into the commercial business , which it quit in 1981 after losing its
+BRINGING HOME THE

best_formal.txt

@@ -0,0 +1,6 @@
+Images from television of ten fired her imagination
+Home truths
+SWEENEY BIG Cheese
+WORLD PARTY animal
+Apart from Fedorov, that fellow who gave us the slip
+I beat a heavy retreat

config/local.json

@@ -1,10 +1,10 @@
 {
     "allow_loading_dataset_files": true,
-    "preloaded_dataset_filename": "sample_input.txt",
+    "preloaded_dataset_filename": "all.txt",
     "debug": true,
     "models": {
         "": null,
-
+        "meta-llama/Llama-3.2-1B-Instruct": null,
         "meta-llama/Meta-Llama-3-8B": null,
         "meta-llama/Meta-Llama-3-70B": null,
         "meta-llama/Meta-Llama-3-8B-Instruct": null,
@@ -51,6 +51,6 @@
         "facebook/opt-13b": null,
         "facebook/opt-30b": null
     },
-    "default_model": "",
+    "default_model": "meta-llama/Llama-3.2-1B-Instruct",
     "demo_mode": false
 }

corr2false_formal.txt

@@ -0,0 +1,8 @@
+Pleased with the group's healthy sales, Red Rhino allowed the band the luxury of a colour sleeve for their third release, the double A-side, ‘This Boy Can Wait’ and ‘You Should Always Keep In Touch
+Whitbread had no formal education and seems to have been the archetypal self-made man
+Their answers result in the supreme irony of lorry-loads of young men, having scraped together
+The Society is keeping in close touch
+In this instance we are again studying a very real struggle, with gangs looting, scavenging and turning to organised crime as a means of scraping together
+But Mr Kaifu is in no mood to let anyone steal his thunder
+With me that rings a warning bell
+Like the constable in the story, use any cover to keep your attacker at bay

env.yaml

@@ -6,7 +6,8 @@ channels:
 dependencies:
   - python=3.12
   - pytorch
-  - pytorch-cuda=11.8
+  #- pytorch-cuda=11.8 # Jana: Resolve version conflict with idiom installations
+  - pytorch-cuda
   - nodejs
   - yarn
   - pip

false2corr_formal.txt

@@ -0,0 +1,9 @@
+Organisers of the biggest air rally in Europe say that military red tape
+Earlier in this chapter ( p. 70 ) striking a realistic balance
+Inevitably, a book such as this harps on
+The bottom line is
+Rachel kept in touch
+It took me breath away
+( James Brown, 20/2/88 who made it Single Of The Week along with The Wedding Present's ‘Nobody's Twisting Your Arm
+The achievements are well recorded : the Brabazon airliner, the abortive TSR2 fighter plane and Concorde — the fastest white elephant
+The word is much used by his closest adviser, Hernando de Soto, author of ‘The Other Path’ — a celebrated study of Peru's huge and dense informal sector of street vendors, cottage industries and red tape

llm_transparency_tool/models/tlens_model.py

@@ -126,7 +126,8 @@ def _model(self):
             tlens_model.set_tokenizer(self.hf_tokenizer, default_padding_side="left")
 
         tlens_model.set_use_attn_result(True)
-        tlens_model.set_use_attn_in(False)
+        # Jana: auskommentiert, weil colab mit GQA einen Fehler geworfen hat
+        #tlens_model.set_use_attn_in(False)
         tlens_model.set_use_split_qkv_input(False)
 
         return tlens_model
@@ -158,24 +159,24 @@ def batch_size(self) -> int:
             raise self._run_exception
         return self._last_run.logits.shape[0]
 
-    @typechecked
+    #@typechecked # Jana: commented out because of colab issues, TODO: in documentation aufnehmen
     def tokens(self) -> Int[torch.Tensor, "batch pos"]:
         if not self._last_run:
             raise self._run_exception
         return self._last_run.tokens
 
-    @typechecked
+    #@typechecked
     def tokens_to_strings(self, tokens: Int[torch.Tensor, "pos"]) -> List[str]:
         return self._model.to_str_tokens(tokens)
 
-    @typechecked
+    #@typechecked
     def logits(self) -> Float[torch.Tensor, "batch pos d_vocab"]:
         if not self._last_run:
             raise self._run_exception
         return self._last_run.logits
 
     @torch.no_grad()
-    @typechecked
+    #@typechecked
     def unembed(
         self,
         t: Float[torch.Tensor, "d_model"],
@@ -197,36 +198,36 @@ def _get_block(self, layer: int, block_name: str) -> torch.Tensor:
 
     # ================= Methods related to the residual stream =================
 
-    @typechecked
+    #@typechecked
     def residual_in(self, layer: int) -> Float[torch.Tensor, "batch pos d_model"]:
         if not self._last_run:
             raise self._run_exception
         return self._get_block(layer, "hook_resid_pre")
 
-    @typechecked
+    #@typechecked
     def residual_after_attn(
         self, layer: int
     ) -> Float[torch.Tensor, "batch pos d_model"]:
         if not self._last_run:
             raise self._run_exception
         return self._get_block(layer, "hook_resid_mid")
 
-    @typechecked
+    #@typechecked
     def residual_out(self, layer: int) -> Float[torch.Tensor, "batch pos d_model"]:
         if not self._last_run:
             raise self._run_exception
         return self._get_block(layer, "hook_resid_post")
 
     # ================ Methods related to the feed-forward layer ===============
 
-    @typechecked
+    #@typechecked
     def ffn_out(self, layer: int) -> Float[torch.Tensor, "batch pos d_model"]:
         if not self._last_run:
             raise self._run_exception
         return self._get_block(layer, "hook_mlp_out")
 
     @torch.no_grad()
-    @typechecked
+    #@typechecked
     def decomposed_ffn_out(
         self,
         batch_i: int,
@@ -238,7 +239,7 @@ def decomposed_ffn_out(
         processed_activations = self._get_block(layer, "mlp.hook_post")[batch_i][pos]
         return torch.mul(processed_activations.unsqueeze(-1), self._model.blocks[layer].mlp.W_out)
 
-    @typechecked
+    #@typechecked
     def neuron_activations(
         self,
         batch_i: int,
@@ -247,7 +248,7 @@ def neuron_activations(
     ) -> Float[torch.Tensor, "hidden"]:
         return self._get_block(layer, "mlp.hook_pre")[batch_i][pos]
 
-    @typechecked
+    #@typechecked
     def neuron_output(
         self,
         layer: int,
@@ -257,13 +258,13 @@ def neuron_output(
 
     # ==================== Methods related to the attention ====================
 
-    @typechecked
+    #@typechecked
     def attention_matrix(
         self, batch_i: int, layer: int, head: int
     ) -> Float[torch.Tensor, "query_pos key_pos"]:
         return self._get_block(layer, "attn.hook_pattern")[batch_i][head]
 
-    @typechecked
+    #@typechecked
     def attention_output_per_head(
         self,
         batch_i: int,
@@ -273,7 +274,7 @@ def attention_output_per_head(
     ) -> Float[torch.Tensor, "d_model"]:
         return self._get_block(layer, "attn.hook_result")[batch_i][pos][head]
 
-    @typechecked
+    #@typechecked
     def attention_output(
         self,
         batch_i: int,
@@ -283,7 +284,7 @@ def attention_output(
         return self._get_block(layer, "hook_attn_out")[batch_i][pos]
 
     @torch.no_grad()
-    @typechecked
+    #@typechecked
     def decomposed_attn(
         self, batch_i: int, layer: int
     ) -> Float[torch.Tensor, "pos key_pos head d_model"]:

llm_transparency_tool/routes/contributions.py

@@ -13,7 +13,7 @@
 
 
 @torch.no_grad()
-@typechecked
+#@typechecked
 def get_contributions(
     parts: torch.Tensor,
     whole: torch.Tensor,
@@ -53,7 +53,7 @@ def get_contributions(
 
 
 @torch.no_grad()
-@typechecked
+#@typechecked
 def get_contributions_with_one_off_part(
     parts: torch.Tensor,
     one_off: torch.Tensor,
@@ -90,7 +90,7 @@ def get_contributions_with_one_off_part(
 
 
 @torch.no_grad()
-@typechecked
+#@typechecked
 def get_attention_contributions(
     resid_pre: Float[torch.Tensor, "batch pos d_model"],
     resid_mid: Float[torch.Tensor, "batch pos d_model"],
@@ -125,7 +125,7 @@ def get_attention_contributions(
 
 
 @torch.no_grad()
-@typechecked
+#@typechecked
 def get_mlp_contributions(
     resid_mid: Float[torch.Tensor, "batch pos d_model"],
     resid_post: Float[torch.Tensor, "batch pos d_model"],
@@ -143,7 +143,7 @@ def get_mlp_contributions(
 
 
 @torch.no_grad()
-@typechecked
+#@typechecked
 def get_decomposed_mlp_contributions(
     resid_mid: Float[torch.Tensor, "d_model"],
     resid_post: Float[torch.Tensor, "d_model"],

wrong_formal.txt

@@ -0,0 +1,12 @@
+SWEENEY BIG Cheese
+But it had been years, even then, since Elvis was top of the Hit Parade, and if all he had to offer the world was slicked-back hair and a sulky pout, the Dead Loss
+Not only in Europe, but also in Afghanistan, Africa and East Asia, the Soviet Empire now drew in its horns
+I beat a heavy retreat
+Home truths
+Why not cut the Mustard
+Apart from Fedorov, that fellow who gave us the slip
+While the credit- driven Anglo economies draw in their horns
+Old chestnut
+I call the tunes
+The writer often called the tune
+Far from drawing in his horns