diff --git a/tests/distributed/test_context_parallel.py b/tests/distributed/test_context_parallel.py
new file mode 100644
index 000000000..b65c8fb47
--- /dev/null
+++ b/tests/distributed/test_context_parallel.py
@@ -0,0 +1,76 @@
+# Copyright (c) 2025, EleutherAI
+# Licensed under the Apache 2.0 license.
+
+"""
+Unit‑tests for context‑parallelism
+
+We patch `megatron.mpu.get_context_parallel_*` so that we don't have to set up distributed on the gh CI runner
+2‑way context‑parallel world is running, then verify that:
+
+1. `zigzag_data` returns the correct slice for each (fake) rank.
+2. `RotaryEmbedding` builds `cos_cached` / `sin_cached` using the same
+   zig‑zag time‑indices.
+
+"""
+
+import torch
+import pytest
+import megatron.mpu as mpu
+from megatron.mpu.data import zigzag_data
+from megatron.model.positional_embeddings import RotaryEmbedding
+
+
+@pytest.mark.parametrize("rank", [0, 1])
+def test_zigzag_and_rotary(monkeypatch, rank):
+    """
+    Simulate a 2‑GPU context‑parallel group and check that both the low‑level
+    zig‑zag utility and the higher‑level rotary‑embedding cache behave as
+    expected on each rank.
+    """
+    # Patch the MPU helpers to fake a 2‑way group
+    monkeypatch.setattr(mpu, "get_context_parallel_world_size", lambda: 2)
+    monkeypatch.setattr(mpu, "get_context_parallel_rank", lambda: rank)
+
+    # zigzag_data
+    seq_dim = 1
+    x = torch.arange(16).view(2, 8)        # shape: (batch=2, seq=8)
+
+    # Compute the expected zig‑zag slice manually
+    chunks = torch.chunk(x, 2 * 2, dim=seq_dim)   # 4 chunks of length 2
+    expected = (
+        torch.cat((chunks[0], chunks[-1]), dim=seq_dim)
+        if rank == 0
+        else torch.cat((chunks[1], chunks[-2]), dim=seq_dim)
+    )
+
+    out = zigzag_data(x, seq_dim=seq_dim)
+    assert torch.equal(out, expected), "zig‑zag sharding mismatch"
+
+    # RotaryEmbedding cache
+    dim = 8
+    rope = RotaryEmbedding(
+        dim=dim,
+        max_seq_len=8,
+        base=10_000,
+        precision=torch.float32,
+        zigzag=True,
+    )
+
+    # Re‑create the ‘t’ indices that _prepare_cache() should have used
+    full_t = torch.arange(8)
+    expected_t = (
+        torch.cat((full_t[:2], full_t[-2:]))     # rank 0
+        if rank == 0
+        else torch.cat((full_t[2:4], full_t[-4:-2]))  # rank 1
+    )
+
+    inv_freq = 1.0 / (10_000 ** (torch.arange(0, dim, 2).float() / dim))
+    freqs = torch.einsum("i,j->ij", expected_t, inv_freq)
+    emb = torch.cat((freqs, freqs), dim=-1)
+    cos_ref, sin_ref = emb.cos(), emb.sin()
+
+    assert rope.cos_cached.shape == cos_ref.shape
+    assert rope.sin_cached.shape == sin_ref.shape
+    assert torch.allclose(rope.cos_cached, cos_ref, atol=1e-6)
+    assert torch.allclose(rope.sin_cached, sin_ref, atol=1e-6)
+
diff --git a/tests/model/test_dmoe.py b/tests/model/test_dmoe.py
new file mode 100644
index 000000000..daeda4d58
--- /dev/null
+++ b/tests/model/test_dmoe.py
@@ -0,0 +1,107 @@
+# Copyright (c) 2025, EleutherAI
+# Licensed under the Apache 2.0 licence.
+
+"""
+▸ Part 1 – expert‑token helper utilities:
+    * `get_expert_tokens_for_rank`
+    * `get_expert_token_counts_for_rank`
+
+▸ Part 2 – lightweight router (`TopKTokenChoiceRouter`)
+    * shape & range of returned weights / indices
+    * determinism under identical input
+
+"""
+
+import types
+import torch
+import pytest
+import importlib
+
+
+@pytest.fixture(autouse=True)
+def patch_mpu(monkeypatch):
+    """
+    Pretend we have a 2‑way tensor‑parallel group; most MoE helpers only query
+    `get_model_parallel_world_size` and `get_model_parallel_rank`.
+    """
+    import megatron.mpu as mpu
+
+    monkeypatch.setattr(mpu, "get_model_parallel_world_size", lambda: 2, raising=False)
+    # `rank` will be injected per‑test case
+    yield
+
+
+def _set_rank(monkeypatch, rank: int):
+    import megatron.mpu as mpu
+    monkeypatch.setattr(mpu, "get_model_parallel_rank", lambda: rank, raising=False)
+
+
+# Part 1 – expert‑token split / gather helpers
+@pytest.mark.parametrize("rank", [0, 1])
+def test_expert_token_helpers(monkeypatch, rank):
+    """
+    A tiny batch of 6 routed tokens divided among 4 experts with the pattern
+    [2,1,0,3].  With world_size==2 each rank owns 2 experts ⇒ verify that
+     the expected slices/counts are returned.
+    """
+    from megatron.mpu.initialize import (
+        get_expert_tokens_for_rank,
+        get_expert_token_counts_for_rank,
+    )
+
+    _set_rank(monkeypatch, rank)
+
+    tokens_per_expert = torch.tensor([2, 1, 0, 3])          # len == num_experts
+    routed          = torch.arange(6*3).view(6, 3)          # shape (6, 3)
+
+    # ‑‑ expected slice for this fake rank
+    # cumulative sums → [2,3,3,6]; rank 0 gets experts 0&1, rank 1 gets 2&3
+    start = 0 if rank == 0 else 3
+    end   = 3 if rank == 0 else 6
+    want_slice = routed[start:end]
+
+    out_tokens = get_expert_tokens_for_rank(routed, tokens_per_expert)
+    out_counts = get_expert_token_counts_for_rank(tokens_per_expert)
+
+    assert torch.equal(out_tokens, want_slice)
+    assert out_counts.tolist() == ([2, 1] if rank == 0 else [0, 3])
+
+
+# Part 2 – Top‑K token‑choice router
+def _dummy_args(num_experts=8, top_k=2, hidden_size=16):
+    """Return a minimal object that TopKTokenChoiceRouter expects."""
+    return types.SimpleNamespace(
+        hidden_size      = hidden_size,
+        moe_num_experts  = num_experts,
+        moe_top_k        = top_k,
+        moe_jitter_eps   = None,
+        params_dtype     = torch.float32,    # keep everything on CPU
+    )
+
+
+@pytest.mark.parametrize("top_k", [1, 2])
+def test_router_shapes_and_range(top_k):
+    """Router must return (batch, top_k) tensors; indices < num_experts."""
+    mod = importlib.import_module("megatron.model.router")
+    Router = mod.TopKTokenChoiceRouter
+
+    args  = _dummy_args(num_experts=5, top_k=top_k, hidden_size=32)
+    router = Router(args, init_method=torch.nn.init.uniform_)
+
+    seq, bs = 4, 3
+    x = torch.randn(seq, bs, args.hidden_size)
+
+    w, idx = router(x)
+
+    assert w.shape == (seq * bs, top_k)
+    assert idx.shape == (seq * bs, top_k)
+    assert torch.all(idx < args.moe_num_experts)
+    # Probabilities must be positive and ≤1
+    assert torch.all(w >= 0) and torch.all(w <= 1)
+
+    # Deterministic behaviour for identical input (no jitter, eval mode).
+    router.eval()
+    w2, idx2 = router(x)
+    assert torch.equal(w, w2)
+    assert torch.equal(idx, idx2)
+
diff --git a/tests/model/test_mup.py b/tests/model/test_mup.py
new file mode 100644
index 000000000..ab1770384
--- /dev/null
+++ b/tests/model/test_mup.py
@@ -0,0 +1,80 @@
+# Copyright (c) 2025, EleutherAI
+# Licensed under the Apache 2.0 license.
+
+import types
+import torch
+import pytest
+
+from megatron.model.utils import get_params_for_weight_decay_optimization
+from megatron.learning_rates import AnnealingLR
+
+
+class TinyNet(torch.nn.Module):
+    """Just enough structure to exercise the param‑group builder."""
+    def __init__(self):
+        super().__init__()
+        self.lin = torch.nn.Linear(4, 4)           # should get weight‑decay
+        self.norm = torch.nn.LayerNorm(4)          # should be no‑decay
+
+
+@pytest.fixture(scope="module")
+def dummy_args():
+    # Only the attributes that `get_params_for_weight_decay_optimization`
+    # actually accesses.
+    return types.SimpleNamespace(weight_decay=0.1)
+
+
+def _new_scheduler(optimizer, use_mup, width_mult):
+    """
+    Construct an AnnealingLR and monkey‑patch ``get_lr`` so the test is
+    independent of the exact schedule math.
+    """
+    sched = AnnealingLR(
+        optimizer,
+        start_lr=0.0,
+        max_lr=0.02,
+        min_lr=0.0,
+        warmup_iter=0,
+        total_iters=1,
+        decay_style="constant",
+        use_checkpoint_lr_scheduler=False,
+        override_lr_scheduler=False,
+        use_mup=use_mup,
+        mup_width_multiplier=width_mult,
+    )
+    # Force the scheduler to think LR should be 0.02 every step
+    AnnealingLR.get_lr = lambda self: 0.02
+    return sched
+
+
+def test_param_groups_have_lr_adjust(dummy_args):
+    """Builder should tag both WD and no‑WD groups with ``lr_adjust``."""
+    net = TinyNet()
+    groups = get_params_for_weight_decay_optimization(net, dummy_args)
+
+    assert len(groups) == 2
+    assert all(g.get("lr_adjust", False) for g in groups), (
+        "Every param‑group returned by the builder must carry lr_adjust=True "
+        "so muP knows to divide its LR."
+    )
+
+
+@pytest.mark.parametrize("use_mup,expected_factor", [(True, 4.0), (False, 1.0)])
+def test_scheduler_scales_learning_rate(monkeypatch, dummy_args, use_mup, expected_factor):
+    """
+    When `use_mup` is True the LR of *lr_adjust* groups must be divided by
+    ``mup_width_multiplier``; otherwise, it must stay unchanged.
+    """
+    net = TinyNet()
+    param_groups = get_params_for_weight_decay_optimization(net, dummy_args)
+
+    optimizer = torch.optim.SGD(param_groups, lr=0.0) # fine for sanity checking
+    width_mult = 4.0
+    sched = _new_scheduler(optimizer, use_mup=use_mup, width_mult=width_mult)
+
+    sched.step()
+
+    lrs = [g["lr"] for g in optimizer.param_groups]
+    assert pytest.approx(lrs[0], rel=1e-7) == 0.02 / expected_factor
+    assert pytest.approx(lrs[1], rel=1e-7) == 0.02 / expected_factor
+