diff --git a/tests/unit/model_bridge/supported_architectures/test_starcoder2_adapter.py b/tests/unit/model_bridge/supported_architectures/test_starcoder2_adapter.py
index 7718cb1c8..6e021fb11 100644
--- a/tests/unit/model_bridge/supported_architectures/test_starcoder2_adapter.py
+++ b/tests/unit/model_bridge/supported_architectures/test_starcoder2_adapter.py
@@ -13,17 +13,23 @@
     SUPPORTED_ARCHITECTURES,
 )
 from transformer_lens.model_bridge.generalized_components import (
+    BlockBridge,
+    EmbeddingBridge,
     GatedMLPBridge,
+    LinearBridge,
     MLPBridge,
     NormalizationBridge,
+    PositionEmbeddingsAttentionBridge,
     RMSNormalizationBridge,
+    RotaryEmbeddingBridge,
+    UnembeddingBridge,
 )
 from transformer_lens.model_bridge.supported_architectures.starcoder2 import (
     Starcoder2ArchitectureAdapter,
 )
 
 
-def _make_cfg() -> TransformerBridgeConfig:
+def _make_cfg(n_key_value_heads: int | None = 2) -> TransformerBridgeConfig:
     return make_bridge_cfg(
         "Starcoder2ForCausalLM",
         d_model=64,
@@ -33,7 +39,7 @@ def _make_cfg() -> TransformerBridgeConfig:
         n_heads=4,
         d_mlp=256,
         d_vocab=512,
-        n_key_value_heads=2,
+        n_key_value_heads=n_key_value_heads,
         default_prepend_bos=True,
     )
 
@@ -59,14 +65,56 @@ def test_qkv_bias_conversions_use_kv_head_count(self, adapter):
             assert key in conv, f"missing {key}"
             assert conv[key].tensor_conversion.axes_lengths["h"] == adapter.cfg.n_key_value_heads
 
+    def test_conversion_key_set(self, adapter):
+        """q/k/v get both a weight and a per-head bias conversion; o's bias stays
+        [d_model], so it must not gain a per-head reshape."""
+        assert set(adapter.weight_processing_conversions) == {
+            "blocks.{i}.attn.q.weight",
+            "blocks.{i}.attn.k.weight",
+            "blocks.{i}.attn.v.weight",
+            "blocks.{i}.attn.o.weight",
+            "blocks.{i}.attn.q.bias",
+            "blocks.{i}.attn.k.bias",
+            "blocks.{i}.attn.v.bias",
+        }
+
+    def test_missing_kv_heads_falls_back_to_n_heads(self):
+        """MHA checkpoints omit n_key_value_heads; the bias reshapes must still
+        be emitted, split by n_heads."""
+        adapter = Starcoder2ArchitectureAdapter(_make_cfg(n_key_value_heads=None))
+        conv = adapter.weight_processing_conversions
+        assert conv["blocks.{i}.attn.k.bias"].tensor_conversion.axes_lengths["h"] == 4
+
 
 class TestStarcoder2ComponentMapping:
+    def test_top_level_mapping(self, adapter):
+        mapping = adapter.component_mapping
+        assert isinstance(mapping["embed"], EmbeddingBridge)
+        assert isinstance(mapping["rotary_emb"], RotaryEmbeddingBridge)
+        assert isinstance(mapping["blocks"], BlockBridge)
+        assert isinstance(mapping["unembed"], UnembeddingBridge)
+        assert mapping["embed"].name == "model.embed_tokens"
+        assert mapping["blocks"].name == "model.layers"
+        assert mapping["unembed"].name == "lm_head"
+
+    def test_attention_is_separate_qkvo(self, adapter):
+        """Separate q/k/v/o projections — unlike GPTBigCode's fused c_attn."""
+        attn = adapter.component_mapping["blocks"].submodules["attn"]
+        assert isinstance(attn, PositionEmbeddingsAttentionBridge)
+        assert attn.name == "self_attn"
+        expected = {"q": "q_proj", "k": "k_proj", "v": "v_proj", "o": "o_proj"}
+        assert set(attn.submodules) == set(expected)
+        for key, hf_name in expected.items():
+            assert isinstance(attn.submodules[key], LinearBridge)
+            assert attn.submodules[key].name == hf_name
+
     def test_norms_are_plain_layernorm(self, adapter):
         """StarCoder2 uses nn.LayerNorm despite its llama-like shape."""
         submodules = adapter.component_mapping["blocks"].submodules
-        assert isinstance(submodules["ln1"], NormalizationBridge)
-        assert not isinstance(submodules["ln1"], RMSNormalizationBridge)
-        assert submodules["ln1"].name == "input_layernorm"
+        for key, hf_name in (("ln1", "input_layernorm"), ("ln2", "post_attention_layernorm")):
+            assert isinstance(submodules[key], NormalizationBridge)
+            assert not isinstance(submodules[key], RMSNormalizationBridge)
+            assert submodules[key].name == hf_name
         assert adapter.component_mapping["ln_final"].name == "model.norm"
 
     def test_mlp_is_plain_c_fc_c_proj(self, adapter):
diff --git a/transformer_lens/model_bridge/supported_architectures/starcoder2.py b/transformer_lens/model_bridge/supported_architectures/starcoder2.py
index fa4acf077..87600d8a3 100644
--- a/transformer_lens/model_bridge/supported_architectures/starcoder2.py
+++ b/transformer_lens/model_bridge/supported_architectures/starcoder2.py
@@ -3,6 +3,9 @@
 BigCode's StarCoder2 (``Starcoder2ForCausalLM``): pre-norm decoder with
 plain LayerNorm (not RMS), separate biased q/k/v/o projections, GQA, RoPE,
 and a non-gated ``c_fc``/``c_proj`` MLP.
+
+Not a drop-in for its GPTBigCode predecessor: that one fuses q/k/v into a
+single ``c_attn`` and uses learned positions, so it needs a different bridge.
 """
 
 from typing import Any
diff --git a/transformer_lens/tools/model_registry/generate_report.py b/transformer_lens/tools/model_registry/generate_report.py
index 559425cf2..2f39db8d7 100644
--- a/transformer_lens/tools/model_registry/generate_report.py
+++ b/transformer_lens/tools/model_registry/generate_report.py
@@ -79,6 +79,7 @@
     "OlmoHybridForCausalLM": "Allen AI's OLMo Hybrid (GatedDeltaNet linear attention + full attention)",
     "StableLmForCausalLM": "Stability AI's StableLM model",
     "SmolLM3ForCausalLM": "Hugging Face's SmolLM3 compact open model with NoPE layers",
+    "Starcoder2ForCausalLM": "BigCode's StarCoder2 code generation model",
     "T5ForConditionalGeneration": "Google's T5 encoder-decoder model (partial support)",
     "T5WithLMHeadModel": "Legacy T5 class name on old google-t5 checkpoints (t5-3b, t5-11b)",
     "T5GemmaForConditionalGeneration": "Google's T5Gemma encoder-decoder model with Gemma-style RoPE, GQA, and gated MLP",