Release 2.5

.github/ISSUE_TEMPLATE/bug.md

@@ -17,11 +17,11 @@ Please try to provide a minimal example to reproduce the bug. Error messages and
 Describe the characteristic of your environment:
  * Describe how `transformer_lens` was installed (pip, docker, source, ...)
  * What OS are you using? (Linux, MacOS, Windows) 
- * Python version (We suppourt 3.7 -3.10 currently)
+ * Python version (We support 3.7--3.10 currently)
 
 **Additional context**
 Add any other context about the problem here.
 
 ### Checklist
 
-- [ ] I have checked that there is no similar [issue](https://github.com/TransformerLensOrg/TransformerLens/issues) in the repo (**required**)
+- [ ] I have checked that there is no similar [issue](https://github.com/TransformerLensOrg/TransformerLens/issues) in the repo (**required**)

transformer_lens/HookedTransformer.py

@@ -1070,6 +1070,7 @@ def from_pretrained(
         default_prepend_bos: bool = True,
         default_padding_side: Literal["left", "right"] = "right",
         dtype="float32",
+        first_n_layers: Optional[int] = None,
         **from_pretrained_kwargs,
     ) -> "HookedTransformer":
         """Load in a Pretrained Model.
@@ -1204,6 +1205,7 @@ def from_pretrained(
                 the model.
             default_padding_side: Which side to pad on when tokenizing. Defaults to
                 "right".
+            first_n_layers: If specified, only load the first n layers of the model.
         """
 
         assert not (
@@ -1261,6 +1263,7 @@ def from_pretrained(
             n_devices=n_devices,
             default_prepend_bos=default_prepend_bos,
             dtype=dtype,
+            first_n_layers=first_n_layers,
             **from_pretrained_kwargs,
         )
 

transformer_lens/HookedTransformerConfig.py

@@ -78,7 +78,7 @@ class HookedTransformerConfig:
             attention
         attn_types (List[str], *optional*): the types of attention to use for
             local attention
-        weight_init_mode (str): the initialization mode to use for the
+        init_mode (str): the initialization mode to use for the
             weights. Only relevant for custom models, ignored for pre-trained.
             We now support 'gpt2', 'xavier_uniform', 'xavier_normal', 'kaiming_uniform',
             'kaiming_normal'. MuP support to come. Defaults to 'gpt2'.
@@ -100,7 +100,7 @@ class HookedTransformerConfig:
             Used to set sources of randomness (Python, PyTorch and NumPy) and to initialize weights.
             Defaults to None. We recommend setting a seed, so your experiments are reproducible.
         initializer_range (float): The standard deviation of the normal used to
-            initialise the weights, initialized to 0.8 / sqrt(d_model). If weight_init_mode is
+            initialise the weights, initialized to 0.8 / sqrt(d_model). If init_mode is
             'xavier_uniform' or 'xavier_normal', this value is instead treated as the `gain` parameter for the weight
             initialisation (a constant factor to scale the weights by). Defaults to -1.0, which means not set.
         init_weights (bool): Whether to initialize the weights. Defaults to

transformer_lens/loading_from_pretrained.py

@@ -1389,6 +1389,7 @@ def get_pretrained_model_config(
     n_devices: int = 1,
     default_prepend_bos: bool = True,
     dtype: torch.dtype = torch.float32,
+    first_n_layers: Optional[int] = None,
     **kwargs,
 ):
     """Returns the pretrained model config as an HookedTransformerConfig object.
@@ -1501,6 +1502,8 @@ def get_pretrained_model_config(
     cfg_dict["default_prepend_bos"] = default_prepend_bos
     if hf_cfg is not None:
         cfg_dict["load_in_4bit"] = hf_cfg.get("quantization_config", {}).get("load_in_4bit", False)
+    if first_n_layers is not None:
+        cfg_dict["n_layers"] = first_n_layers
 
     cfg = HookedTransformerConfig.from_dict(cfg_dict)
     return cfg