diff --git a/pkg/llms_from_scratch/tests/test_qwen3.py b/pkg/llms_from_scratch/tests/test_qwen3.py
index 82956de..b64ea8e 100644
--- a/pkg/llms_from_scratch/tests/test_qwen3.py
+++ b/pkg/llms_from_scratch/tests/test_qwen3.py
@@ -71,7 +71,7 @@ def dummy_cfg_base():
         "n_kv_groups": 1,
         "qk_norm": False,
         "dtype": torch.float32,
-        "rope_base": 10000,
+        "rope_base": 1000000,
         "context_length": 64,
         "num_experts": 0,
     }
@@ -143,18 +143,21 @@ def test_qwen3_kvcache_equivalence(cfg_name, request):
 
 
 @pytest.mark.skipif(not transformers_installed, reason="transformers not installed")
-def test_rope():
+@pytest.mark.parametrize("context_len", [1024, 8192, 40960])
+def test_rope(context_len):
 
-    from transformers.models.qwen3.modeling_qwen3 import Qwen3RotaryEmbedding, apply_rotary_pos_emb
+    from transformers.models.qwen3.modeling_qwen3 import (
+        Qwen3RotaryEmbedding,
+        apply_rotary_pos_emb,
+    )
 
     # Settings
     batch_size = 1
-    context_len = 8192
     num_heads = 4
     head_dim = 16
     rope_theta = 1_000_000
 
-    # Instantiate RoPE parameters
+    # Instantiate RoPE parameters (our implementation)
     cos, sin = compute_rope_params(
         head_dim=head_dim,
         theta_base=rope_theta,
@@ -166,7 +169,7 @@ def test_rope():
     queries = torch.randn(batch_size, num_heads, context_len, head_dim)
     keys = torch.randn(batch_size, num_heads, context_len, head_dim)
 
-    # Apply rotary position embeddings
+    # Apply rotary embeddings with our implementation
     queries_rot = apply_rope(queries, cos, sin)
     keys_rot = apply_rope(keys, cos, sin)
 
@@ -176,7 +179,7 @@ def test_rope():
         factor = 1.0
         dim: int = head_dim
         rope_theta = 1_000_000
-        max_position_embeddings: int = 8192
+        max_position_embeddings = context_len
         hidden_size = head_dim * num_heads
         num_attention_heads = num_heads
 
@@ -187,10 +190,17 @@ def test_rope():
     ref_cos, ref_sin = rot_emb(queries, position_ids)
     ref_queries_rot, ref_keys_rot = apply_rotary_pos_emb(queries, keys, ref_cos, ref_sin)
 
-    torch.testing.assert_close(sin, ref_sin.squeeze(0))
-    torch.testing.assert_close(cos, ref_cos.squeeze(0))
-    torch.testing.assert_close(keys_rot, ref_keys_rot)
-    torch.testing.assert_close(queries_rot, ref_queries_rot)
+    # torch.testing.assert_close(sin, ref_sin.squeeze(0), rtol=1e-5, atol=1e-6)
+    # torch.testing.assert_close(cos, ref_cos.squeeze(0), rtol=1e-5, atol=1e-6)
+
+    # torch.testing.assert_close(keys_rot, ref_keys_rot, rtol=1e-5, atol=1e-6)A
+    # torch.testing.assert_close(queries_rot, ref_queries_rot, rtol=1e-5, atol=1e-6)
+
+    assert torch.equal(sin, ref_sin.squeeze(0))
+    assert torch.equal(cos, ref_cos.squeeze(0))
+
+    assert torch.equal(keys_rot, ref_keys_rot)
+    assert torch.equal(queries_rot, ref_queries_rot)
 
 
 @pytest.fixture(scope="session")