diff --git a/docs/source/examples/huggingface.rst b/docs/source/examples/huggingface.rst
index 68d88446..01baa543 100644
--- a/docs/source/examples/huggingface.rst
+++ b/docs/source/examples/huggingface.rst
@@ -7,7 +7,7 @@ One way to do this would be to manually apply a data parallel wrapper (like DDP
 
 Instead we recommend converting your HuggingFace checkpoint into a format that can be loaded into an equivalent OLMo-core :class:`~olmo_core.nn.transformer.Transformer` model, when possible, using the functions provided by :mod:`olmo_core.distributed.checkpoint`.
 
-Below is an example that shows how to convert a Llama-3.2 checkpoint on HuggingFace into the right format for OLMo-core.
+Below is an example that shows how to convert an OLMo2 or Llama-3 checkpoint on HuggingFace into the right format for OLMo-core.
 It would be straight forward to adapt this script to convert in the other direction as well.
 
 .. seealso::
diff --git a/src/examples/huggingface/convert_checkpoint.py b/src/examples/huggingface/convert_checkpoint.py
index 34ed07e1..6559ee43 100644
--- a/src/examples/huggingface/convert_checkpoint.py
+++ b/src/examples/huggingface/convert_checkpoint.py
@@ -1,8 +1,8 @@
 """
-Example script showing how you could convert model weights on HuggingFace for a Llama-3.2 model
-into a format that can be loaded by OLMo-core for fine-tuning.
+Example script showing how you could convert model weights on HuggingFace for an OLMo2 or Llama-3.*
+model into a format that can be loaded by OLMo-core for fine-tuning.
 
-Note that this script is architecture-dependent, meaning it may only work for Llama-3.2 models on
+Note that this script is architecture-dependent, meaning it may only work for OLMo2/Llama models on
 HuggingFace.
 """
 
@@ -20,14 +20,38 @@
 
 log = logging.getLogger(__name__)
 
-HF_MODEL = "meta-llama/Llama-3.2-1B"
+HF_MODEL = "allenai/OLMo-2-1124-7B"
+# HF_MODEL = "allenai/OLMo-2-1124-7B-Instruct"
+# HF_MODEL = "allenai/OLMo-2-1124-13B-Instruct"
+# HF_MODEL = "meta-llama/Llama-3.2-1B"
+# HF_MODEL = "meta-llama/Llama-3.2-8B"
+
 SAVE_PATH = f"/tmp/checkpoints/{HF_MODEL}"
 SAVE_OVERWRITE = False
 
 TOKENIZER_CONFIG = TokenizerConfig.from_hf(HF_MODEL)
-MODEL_CONFIG = TransformerConfig.llama3_1B(
-    TOKENIZER_CONFIG.vocab_size, fused_ops=False, use_flash=False, rope_scaling=RoPEScalingConfig()
-)
+MODEL_CONFIG: TransformerConfig
+if HF_MODEL == "meta-llama/Llama-3.2-1B":
+    MODEL_CONFIG = TransformerConfig.llama3_1B(
+        TOKENIZER_CONFIG.vocab_size,
+        fused_ops=False,
+        use_flash=False,
+        rope_scaling=RoPEScalingConfig(),
+    )
+elif HF_MODEL.startswith("allenai/OLMo-2-1124-7B"):
+    MODEL_CONFIG = TransformerConfig.olmo2_7B(
+        TOKENIZER_CONFIG.vocab_size,
+        fused_ops=False,
+        use_flash=False,
+    )
+elif HF_MODEL.startswith("allenai/OLMo-2-1124-13B"):
+    MODEL_CONFIG = TransformerConfig.olmo2_13B(
+        TOKENIZER_CONFIG.vocab_size,
+        fused_ops=False,
+        use_flash=False,
+    )
+else:
+    raise NotImplementedError(HF_MODEL)
 
 
 def convert_checkpoint() -> AutoModelForCausalLM:
@@ -78,15 +102,27 @@ def convert_checkpoint() -> AutoModelForCausalLM:
             f"model.layers.{block}.mlp.up_proj.weight"
         )
 
-        # Attention layer norm.
-        new_state_dict[f"blocks.{block}.attention_norm.weight"] = state_dict.pop(
-            f"model.layers.{block}.input_layernorm.weight"
-        )
-
-        # MLP layer norm.
-        new_state_dict[f"blocks.{block}.feed_forward_norm.weight"] = state_dict.pop(
-            f"model.layers.{block}.post_attention_layernorm.weight"
-        )
+        # Layer norms.
+        if "Llama" in HF_MODEL:
+            new_state_dict[f"blocks.{block}.feed_forward_norm.weight"] = state_dict.pop(
+                f"model.layers.{block}.post_attention_layernorm.weight"
+            )
+            new_state_dict[f"blocks.{block}.attention_norm.weight"] = state_dict.pop(
+                f"model.layers.{block}.input_layernorm.weight"
+            )
+        else:
+            new_state_dict[f"blocks.{block}.attention_norm.weight"] = state_dict.pop(
+                f"model.layers.{block}.post_attention_layernorm.weight"
+            )
+            new_state_dict[f"blocks.{block}.feed_forward_norm.weight"] = state_dict.pop(
+                f"model.layers.{block}.post_feedforward_layernorm.weight"
+            )
+            new_state_dict[f"blocks.{block}.attention.q_norm.weight"] = state_dict.pop(
+                f"model.layers.{block}.self_attn.q_norm.weight"
+            )
+            new_state_dict[f"blocks.{block}.attention.k_norm.weight"] = state_dict.pop(
+                f"model.layers.{block}.self_attn.k_norm.weight"
+            )
 
     assert len(state_dict) == 0
 
@@ -97,22 +133,26 @@ def convert_checkpoint() -> AutoModelForCausalLM:
 
 
 def validate_conversion(hf_model):
-    log.info("Loading converted checkpoint for validation...")
-
     device = get_default_device()
 
-    model = MODEL_CONFIG.build(device=device, max_seq_len=131072).eval()
-    load_model_and_optim_state(SAVE_PATH, model)
+    B, T = 1, 120
+    input_ids = torch.randint(0, TOKENIZER_CONFIG.vocab_size, (B, T)).to(device)
 
     hf_model = hf_model.to(device).eval()
+    with torch.no_grad():
+        hf_logits, *_ = hf_model(input_ids=input_ids, return_dict=False)
 
-    B, T = 1, 120
-    input_ids = torch.randint(0, TOKENIZER_CONFIG.vocab_size, (B, T)).to(device)
+    del hf_model
+
+    model = MODEL_CONFIG.build(device=device, max_seq_len=131072).eval()
+
+    log.info("Loading converted checkpoint for validation...")
+    load_model_and_optim_state(SAVE_PATH, model)
 
     with torch.no_grad():
         logits = model(input_ids=input_ids)
-        hf_logits, *_ = hf_model(input_ids=input_ids, return_dict=False)
-        torch.testing.assert_close(hf_logits, logits)
+
+    torch.testing.assert_close(hf_logits, logits)
 
     log.info("Conversion successful")