diff --git a/examples/qualcomm/oss_scripts/llama/__init__.py b/examples/qualcomm/oss_scripts/llama/__init__.py
index 963db6e..9ccfdd0 100644
--- a/examples/qualcomm/oss_scripts/llama/__init__.py
+++ b/examples/qualcomm/oss_scripts/llama/__init__.py
@@ -25,9 +25,14 @@ from executorch.examples.models.granite import (
 from executorch.examples.models.internvl3 import (
     convert_weights as convert_internvl3_weights,
 )
-from executorch.examples.models.phi_4_mini import (
-    convert_weights as convert_phi_4_mini_weights,
-)
+try:
+    from executorch.examples.models.phi_4_mini import (
+        convert_weights as convert_phi_4_mini_weights,
+    )
+except ImportError:
+    # phi_4_mini pulls in torchtune which conflicts with our torchao pin.
+    # We don't need phi for Qwen3 export, so tolerate the missing dep.
+    convert_phi_4_mini_weights = None
 from executorch.examples.models.qwen2_5 import (
     convert_weights as convert_qwen2_5_weights,
 )
@@ -479,6 +484,37 @@ class Qwen3_1_7B(LLMModelConfig):
     quant_recipe = Qwen3_1_7BQuantRecipe
 
 
+@register_llm_model("qwen3-4b")
+@dataclass(init=False, frozen=True)
+class Qwen3_4B(LLMModelConfig):
+    # Local Kazeia addition. Mirrors the Qwen3_1_7B registration; the 4B
+    # variant uses the same convert_weights and 16a4w quant recipe but a
+    # bigger params file. With 4B params at 16a4w the .pte stays under the
+    # 4 GB HTP single-context limit on V79 (empirically ~2.5 GB), so
+    # num_sharding=1 is fine. Compile time on the host is the main cost
+    # (3-4 h on a 16-core x86_64 machine).
+    repo_id: str = "Qwen/Qwen3-4B"
+    params_path: str = os.path.join(
+        BASE_DIR, "../../../models/qwen3/config/4b_config.json"
+    )
+    convert_weights = convert_qwen3_weights
+    transform_weight = False
+    instruct_model = True
+    # num_sharding=1 for hybrid mode: sharding=2 produces a multi-context
+    # .pte (2 graphs × 2 shards = 4 contexts) that the LlmModule load path
+    # can't restore (error 5010 "Context group 1 does not exist"). With
+    # sharding=1 the hybrid export needs ~46 GB RAM peak — the 192 GB swap
+    # on /swapfile handles this; compile takes ~80 min wall but completes
+    # cleanly. Single-context .pte loads fine through the JNI runner.
+    num_sharding = 1
+    masked_softmax = True
+    seq_mse_candidates = 0
+    r1 = False
+    r2 = False
+    r3 = True
+    quant_recipe = Qwen3_1_7BQuantRecipe
+
+
 @register_llm_model("smollm2_135m")
 @dataclass(init=False, frozen=True)
 class Smollm2_135M(LLMModelConfig):
diff --git a/examples/qualcomm/oss_scripts/llama/decoder_constants.py b/examples/qualcomm/oss_scripts/llama/decoder_constants.py
index 74e3959..995c498 100644
--- a/examples/qualcomm/oss_scripts/llama/decoder_constants.py
+++ b/examples/qualcomm/oss_scripts/llama/decoder_constants.py
@@ -55,6 +55,7 @@ DECODER_MODEL_VERSION = {
     "qwen2_5-1_5b": "qwen2_5",
     "qwen3-0_6b": "qwen3",
     "qwen3-1_7b": "qwen3",
+    "qwen3-4b": "qwen3",
     "smollm2_135m": "smollm2_135m",
     "smollm3-3b": "smollm3",
     "glm-1_5b": "glm",