diff --git a/docs/source/kernels.md b/docs/source/kernels.md
index c579d5bd1..356208452 100644
--- a/docs/source/kernels.md
+++ b/docs/source/kernels.md
@@ -5,7 +5,13 @@ Note: cuEquivariance acceleration can be used while DeepSpeed acceleration is en
       cuEquivariance would take precedence, and then would fall back to either DeepSpeed (if enabled) or PyTorch for the shapes it does not handle efficiently.
       Notably, it would fall back for shorter sequences (threshold controlled by `CUEQ_TRIATTN_FALLBACK_THRESHOLD` environment variable), and for shapes with hidden dimension > 128 (diffusion transformer shapes).
 
-To enable, first install OpenFold3 with cuEquivariance: 
+To enable cuequivariance with pixi, use the `openfold3-cuda12-pypi` or `openfold3-cuda13-pypi` environment. Below is a example inference command
+
+```bash
+pixi run -e openfold3-cuda12-pypi run_openfold predict --query-json=query_ubiquitin.json  --runner-yaml=cuequivariance.yml
+```
+
+For other workflows, cuequivariance must first be installed with the cuequivariance optional dependency, e.g.
 
 ```bash
 pip install openfold3[cuequivariance]
@@ -14,6 +20,7 @@ pip install openfold3[cuequivariance]
 Then, to enable these kernels via the runner.yaml, add the following:
 
 ```yaml
+# cuequivariance.yml
 model_update:
   presets: 
     - "predict"
@@ -26,4 +33,4 @@ model_update:
           use_deepspeed_evo_attention: true  # set this to False to use cueq only
 ```
 
-This is specifically for inference, but similar settings can be used for training. 
+This runner.yml is specifically for inference, but similar settings can be used for training. 
diff --git a/openfold3/core/kernels/cueq_utils.py b/openfold3/core/kernels/cueq_utils.py
index ace4d2582..14469185c 100644
--- a/openfold3/core/kernels/cueq_utils.py
+++ b/openfold3/core/kernels/cueq_utils.py
@@ -3,12 +3,14 @@
 import torch
 
 
+def is_cuequivariance_installed() -> bool:
+    """Check if cuequivariance_torch package is installed (regardless of CUDA)."""
+    return importlib.util.find_spec("cuequivariance_torch") is not None
+
+
 def is_cuequivariance_available() -> bool:
     """
     Check if cuequivariance_torch is installed and CUDA is available.
     Even if cuequivariance_torch is installed, it only works with CUDA.
     """
-    return (
-        importlib.util.find_spec("cuequivariance_torch") is not None
-        and torch.cuda.is_available()
-    )
+    return is_cuequivariance_installed() and torch.cuda.is_available()
diff --git a/openfold3/core/model/primitives/attention.py b/openfold3/core/model/primitives/attention.py
index 85681d819..46c924aa1 100644
--- a/openfold3/core/model/primitives/attention.py
+++ b/openfold3/core/model/primitives/attention.py
@@ -746,40 +746,69 @@ def _cueq_triangle_attn(q, k, v, biases, scale):
     )
     mask_bias, triangle_bias = biases
 
-    ##VS: the cueq attn kernel only allows up to 5 input dimensions:
-    ## (batch,*,n_head, *,c_hidden); batch here denotes multiple
-    ## structures in a single fwd pass; while this is fine for the
-    ## pairformer, in the template module we have
-    ## inputs of shape (batch, n_tmpl, n_res,n_head, n_res, c_in)
-    ## so therefore we need to reshape the input to remove the
-    ## extra batch dimension, then reshape it back to the original
+    # cuequivariance triangle_attention expects 5D inputs:
+    #   q/k/v: (B, N, H, S, D)
+    #   bias:  (B, 1, H, S_qo, S_kv)   — N must be 1
+    #   mask:  (B, N, 1, 1, S_kv)
+    #
+    # Inputs arrive here in one of three shapes depending on call path:
+    #   6D — template module: (batch, n_tmpl, N, H, S, D)  → collapse to 5D
+    #   5D — standard pairformer: already correct
+    #   4D — after chunk_layer flattens batch dims: (chunk, H, S, D) → promote to 5D
+
+    # 6D → 5D: merge the (batch, n_tmpl) dims into a single batch dim.
     if len(q.shape) > 5:
         assert len(q.shape) == 6, (
             "max number of dimensions for CUEQ triangle attention kernel is 6"
         )
         is_batched_input = True
-        batch, n_tmpl, n_res, n_head, c_hidden = q.shape[:5]
+        batch, n_tmpl = q.shape[:2]
+        # q: (batch, n_tmpl, N, H, S, D) → (batch*n_tmpl, N, H, S, D)
         q = q.view(batch * n_tmpl, *q.shape[2:])
         k = k.view(batch * n_tmpl, *k.shape[2:])
         v = v.view(batch * n_tmpl, *v.shape[2:])
+        # mask_bias: (batch, n_tmpl, N, 1, 1, S) → (batch*n_tmpl, N, 1, 1, S)
         mask_bias = mask_bias.view(batch * n_tmpl, *mask_bias.shape[2:])
+        # triangle_bias: (batch, n_tmpl, 1, H, S, S) → (batch*n_tmpl, 1, H, S, S)
         triangle_bias = triangle_bias.view(batch * n_tmpl, *triangle_bias.shape[2:])
-    ##VS: The mask for the triangle attention kernel needs to be a
-    ## boolean mask - the default mask is an additive mask, where
-    ## 0 means no masking and -inf means masking. so we need to
-    ## convert this to a boolean mask where positions to keep are
-    ## True, and positions to mask are False.
+
+    # 4D → 5D: chunk_layer flattens batch dims and slices into chunks.
+    # Promote to 5D with N=1 so each chunk entry is an independent batch item.
+    # cuequivariance >=0.8 requires bias shape (B, 1, H, Q, K) with exact
+    # batch match — no implicit broadcasting.
+    is_chunked_input = len(q.shape) == 4
+    if is_chunked_input:
+        # q: (chunk, H, S, D) → (chunk, 1, H, S, D)
+        q = q.unsqueeze(1)
+        k = k.unsqueeze(1)
+        v = v.unsqueeze(1)
+        # mask_bias: (chunk, 1, 1, S) → (chunk, 1, 1, 1, S)
+        mask_bias = mask_bias.unsqueeze(1)
+        # triangle_bias: (chunk, H, S, S) → (chunk, 1, H, S, S)
+        #   or: (1, H, S, S) → (1, 1, H, S, S) when chunk_layer kept B=1
+        triangle_bias = triangle_bias.unsqueeze(1)
+        # chunk_layer skips expanding bias when all its batch dims are 1,
+        # so bias may have B=1 while q has B=chunk. Expand to match.
+        if triangle_bias.shape[0] != q.shape[0]:
+            # (1, 1, H, S, S) → (chunk, 1, H, S, S)
+            triangle_bias = triangle_bias.expand(q.shape[0], *triangle_bias.shape[1:])
+        if mask_bias.shape[0] != q.shape[0]:
+            mask_bias = mask_bias.expand(q.shape[0], *mask_bias.shape[1:])
+
+    # Convert additive mask (0 = keep, -inf = mask) to boolean (True = keep).
     if mask_bias.dtype != torch.bool:
         mask_bias = mask_bias == 0
 
+    # At this point all tensors are 5D with the shapes expected by the kernel.
     o = triangle_attention(q, k, v, bias=triangle_bias, mask=mask_bias, scale=scale)
 
-    if len(q.shape) == 4:
-        ##VS: There's a bug in cueq where if the input is missing the batch dim
-        ## the outputs adds it in and so we need to remove it here
-        o = o.squeeze(0)
+    # Undo the promotions in reverse order.
+    if is_chunked_input:
+        # (chunk, 1, H, S, D) → (chunk, H, S, D)
+        o = o.squeeze(1)
 
     if is_batched_input:
+        # (batch*n_tmpl, N, H, S, D) → (batch, n_tmpl, N, H, S, D)
         o = o.view(batch, n_tmpl, *o.shape[1:])
 
     o = o.transpose(-2, -3)
diff --git a/openfold3/tests/compare_utils.py b/openfold3/tests/compare_utils.py
index c52682b37..41bea60cc 100644
--- a/openfold3/tests/compare_utils.py
+++ b/openfold3/tests/compare_utils.py
@@ -17,7 +17,10 @@
 
 import torch
 
-from openfold3.core.kernels.cueq_utils import is_cuequivariance_available
+from openfold3.core.kernels.cueq_utils import (
+    is_cuequivariance_available,
+    is_cuequivariance_installed,
+)
 
 
 def skip_if_rocm():
@@ -39,9 +42,13 @@ def skip_unless_ds4s_installed():
 
 
 def skip_unless_cueq_installed():
-    return unittest.skipUnless(
-        is_cuequivariance_available(), "Requires CU-Equivaraince to be installed"
-    )
+    if not is_cuequivariance_installed():
+        reason = "Requires cuequivariance to be installed"
+    elif not torch.cuda.is_available():
+        reason = "Requires CUDA (cuequivariance is installed but no GPU available)"
+    else:
+        reason = ""
+    return unittest.skipUnless(is_cuequivariance_available(), reason)
 
 
 def skip_unless_triton_installed():
diff --git a/pixi.lock b/pixi.lock
index 2538ffb7d..de1c4e5e9 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -12,23 +12,23 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.4.22-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.2.25-hbd8a1cb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.5-hecca717_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb03c661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.0-hf4e2dac_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.52.0-hf4e2dac_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_18.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42-h5347b49_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.3-h5347b49_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.2-h35e630c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.1-h35e630c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.4-habeac84_100_cp314.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.14.3-h32b2ec7_101_cp314.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h366c992_103.conda
@@ -40,21 +40,23 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h4777abc_9.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.7.5-hfae3067_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.2.25-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/icu-78.3-hcab7f73_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.45.1-default_h1979696_101.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libexpat-2.7.4-hfae3067_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libffi-3.5.2-h376a255_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/liblzma-5.8.3-he30d5cf_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libmpdec-4.0.0-he30d5cf_1.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.42-h1022ec0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libsqlite-3.52.0-h10b116e_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-15.2.0-hef695bb_18.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.41.3-h1022ec0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.3.2-hdc9db2a_2.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.6.2-h546c87b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.6.1-h546c87b_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pip-26.0.1-pyh145f28c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.14.4-hfd9ac0a_100_cp314.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.14.3-hb06a95a_101_cp314.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/tk-8.6.13-noxft_h0dc03b3_103.conda
@@ -65,18 +67,17 @@ environments:
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       osx-64:
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-      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.4.22-hbd8a1cb_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.7.5-hcc62823_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.2.25-hbd8a1cb_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.6.2-hc881268_0.conda
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-      - conda: https://conda.anaconda.org/conda-forge/osx-64/python-3.14.4-h7c6738f_100_cp314.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.14-8_cp314.conda
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+  license: GPL-3.0-only
+  license_family: GPL
+  purls: []
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-  - liblapacke 3.11.0   6*_openblas
-  - libcblas   3.11.0   6*_openblas
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-  - liblapack  3.11.0   6*_mkl
-  - liblapacke 3.11.0   6*_mkl
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-6_haddc8a3_openblas.conda
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   - liblapacke 3.11.0   6*_openblas
   - libcblas   3.11.0   6*_openblas
+  - mkl <2026
+  license: BSD-3-Clause
+  license_family: BSD
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+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libblas-3.11.0-5_haddc8a3_openblas.conda
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+  - libopenblas >=0.3.30,<1.0a0
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+  - libcblas   3.11.0   5*_openblas
+  - liblapack  3.11.0   5*_openblas
+  - liblapacke 3.11.0   5*_openblas
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-  - liblapack  3.11.0   6*_openblas
-  - blas 2.306   openblas
-  - libcblas   3.11.0   6*_openblas
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   license_family: BSD
   purls: []
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+  - libzlib >=1.3.1,<2.0a0
+  - zstd >=1.5.7,<1.6.0a0
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+  license: BSL-1.0
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libboost-1.90.0-hd24cca6_1.conda
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+  - libxml2 >=2.13.8,<2.14.0a0
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+  license_family: BSD
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+  license: IJG AND BSD-3-Clause AND Zlib
+  purls: []
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+  - blas 2.305   mkl
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-  - libcblas   3.11.0   6*_openblas
+  - liblapacke 3.11.0   5*_openblas
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-  - libcblas   3.11.0   6*_openblas
-  - blas 2.306   openblas
+  - libcblas   3.11.0   5*_openblas
+  - blas 2.305   openblas
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   purls: []
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.11.0-5_hdba1596_mkl.conda
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+  - libcblas 3.11.0 5_hfef963f_mkl
+  - liblapack 3.11.0 5_h5e43f62_mkl
+  constrains:
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+  - blas_mkl
+  license: BSD-3-Clause
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-  - liblapack 3.11.0 6_h88aeb00_openblas
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+  - libcblas 3.11.0 5_hd72aa62_openblas
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+  - blas 2.305   openblas
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   purls: []
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+  - xz 5.8.2.*
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-  sha256: 60a69c7cf1136db33703ce53f810c93c3eca85d117dbe121fa09478355c02007
-  md5: 74369ed16f351dbb59d0545917cd94fa
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/sqlalchemy-2.0.48-py313hc37f9cf_0.conda
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+  md5: eb5d1444f3deb8787413c70a5872c411
   depends:
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   - greenlet !=0.4.17
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   license: MIT
   license_family: MIT
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-  - pkg:pypi/sqlalchemy?source=hash-mapping
-  size: 3852605
-  timestamp: 1775241597035
-- conda: https://conda.anaconda.org/conda-forge/osx-64/sqlalchemy-2.0.49-py313h22ab4a2_0.conda
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-  md5: 221276c9b5de50a922e8f09d0c9d6b28
+  - pkg:pypi/sqlalchemy?source=compressed-mapping
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+  timestamp: 1772645119064
+- conda: https://conda.anaconda.org/conda-forge/osx-64/sqlalchemy-2.0.48-py313h22ab4a2_0.conda
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+  md5: 13494380f3ac4725041c182dc9821ae3
   depends:
   - python
   - greenlet !=0.4.17
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   license: MIT
   license_family: MIT
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-  - pkg:pypi/sqlalchemy?source=hash-mapping
-  size: 3841647
-  timestamp: 1775241415718
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlalchemy-2.0.49-py313h6688731_0.conda
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-  md5: 56fc7ce3494a1077459d51359f5148e7
+  - pkg:pypi/sqlalchemy?source=compressed-mapping
+  size: 3837102
+  timestamp: 1772644920192
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/sqlalchemy-2.0.48-py313h6688731_0.conda
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+  md5: d96ecd4ad18488b26b4c599df74772d9
   depends:
   - python
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@@ -20625,8 +21930,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/sqlalchemy?source=hash-mapping
-  size: 3842925
-  timestamp: 1775241461140
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 - conda: https://conda.anaconda.org/conda-forge/noarch/sqlmodel-0.0.37-pyhcf101f3_0.conda
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   md5: 00e6147bef9a85139099c9861c3b976b
@@ -20698,9 +22003,22 @@ packages:
   license: MIT
   license_family: MIT
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-  - pkg:pypi/tabulate?source=hash-mapping
+  - pkg:pypi/tabulate?source=compressed-mapping
   size: 43964
   timestamp: 1772732795746
+- conda: https://conda.anaconda.org/conda-forge/linux-64/tbb-2022.3.0-h8d10470_1.conda
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+  md5: e3259be3341da4bc06c5b7a78c8bf1bd
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+  - libgcc >=14
+  - libhwloc >=2.12.1,<2.12.2.0a0
+  - libstdcxx >=14
+  license: Apache-2.0
+  license_family: APACHE
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/tbb-2022.3.0-hb700be7_2.conda
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-h06b67a2_5.conda
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/tbb-2021.13.0-hf0c99ee_4.conda
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   depends:
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   - libcxx >=19
-  - libhwloc >=2.12.2,<2.12.3.0a0
+  - libhwloc >=2.12.1,<2.12.2.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 155452
-  timestamp: 1767887347634
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 - conda: https://conda.anaconda.org/conda-forge/noarch/tenacity-9.1.4-pyhcf101f3_0.conda
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@@ -20810,6 +22128,18 @@ packages:
   - pkg:pypi/toml?source=hash-mapping
   size: 24017
   timestamp: 1764486833072
+- conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.0-pyhcf101f3_0.conda
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+  md5: 72e780e9aa2d0a3295f59b1874e3768b
+  depends:
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+  - python
+  license: MIT
+  license_family: MIT
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+  - pkg:pypi/tomli?source=compressed-mapping
+  size: 21453
+  timestamp: 1768146676791
 - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.1-pyhcf101f3_0.conda
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-- pypi: https://download-r2.pytorch.org/whl/cu129/torch-2.11.0%2Bcu129-cp313-cp313-manylinux_2_28_aarch64.whl
+- pypi: https://download-r2.pytorch.org/whl/cu129/torch-2.10.0%2Bcu129-cp313-cp313-manylinux_2_28_aarch64.whl
   name: torch
-  version: 2.11.0+cu129
-  sha256: 20f9a7223804fbc2b39253933a2704756f1bd80529a093fe6e6cbef3341a303e
+  version: 2.10.0+cu129
+  sha256: 08dc9eb950efbf2b65a7973e6d00c8c770d697140296841dc3d86cbc8d372a76
   requires_dist:
   - filelock
   - typing-extensions>=4.10.0
-  - setuptools<82
+  - setuptools ; python_full_version >= '3.12'
   - sympy>=1.13.3
   - networkx>=2.5.1
   - jinja2
   - fsspec>=0.8.5
-  - cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.9.1 ; sys_platform == 'linux'
-  - cuda-bindings>=12.9.4,<13 ; sys_platform == 'linux'
-  - nvidia-cudnn-cu12==9.17.1.4 ; sys_platform == 'linux'
+  - cuda-bindings==12.9.4 ; sys_platform == 'linux'
+  - nvidia-cuda-nvrtc-cu12==12.9.86 ; sys_platform == 'linux'
+  - nvidia-cuda-runtime-cu12==12.9.79 ; sys_platform == 'linux'
+  - nvidia-cuda-cupti-cu12==12.9.79 ; sys_platform == 'linux'
+  - nvidia-cudnn-cu12==9.10.2.21 ; sys_platform == 'linux'
+  - nvidia-cublas-cu12==12.9.1.4 ; sys_platform == 'linux'
+  - nvidia-cufft-cu12==11.4.1.4 ; sys_platform == 'linux'
+  - nvidia-curand-cu12==10.3.10.19 ; sys_platform == 'linux'
+  - nvidia-cusolver-cu12==11.7.5.82 ; sys_platform == 'linux'
+  - nvidia-cusparse-cu12==12.5.10.65 ; sys_platform == 'linux'
   - nvidia-cusparselt-cu12==0.7.1 ; sys_platform == 'linux'
-  - nvidia-nccl-cu12==2.28.9 ; sys_platform == 'linux'
+  - nvidia-nccl-cu12==2.27.5 ; sys_platform == 'linux'
   - nvidia-nvshmem-cu12==3.4.5 ; sys_platform == 'linux'
+  - nvidia-nvtx-cu12==12.9.79 ; sys_platform == 'linux'
+  - nvidia-nvjitlink-cu12==12.9.86 ; sys_platform == 'linux'
+  - nvidia-cufile-cu12==1.14.1.1 ; sys_platform == 'linux'
   - triton==3.6.0 ; sys_platform == 'linux'
   - optree>=0.13.0 ; extra == 'optree'
   - opt-einsum>=3.3 ; extra == 'opt-einsum'
   - pyyaml ; extra == 'pyyaml'
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-- pypi: https://download-r2.pytorch.org/whl/cu129/torch-2.11.0%2Bcu129-cp313-cp313-manylinux_2_28_x86_64.whl
+- pypi: https://download-r2.pytorch.org/whl/cu129/torch-2.10.0%2Bcu129-cp313-cp313-manylinux_2_28_x86_64.whl
   name: torch
-  version: 2.11.0+cu129
-  sha256: fde1830d7f79641680865759dc57780e94a9de7e68a82ed61973e9bc7af29423
+  version: 2.10.0+cu129
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   requires_dist:
   - filelock
   - typing-extensions>=4.10.0
-  - setuptools<82
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   - sympy>=1.13.3
   - networkx>=2.5.1
   - jinja2
   - fsspec>=0.8.5
-  - cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.9.1 ; sys_platform == 'linux'
-  - cuda-bindings>=12.9.4,<13 ; sys_platform == 'linux'
-  - nvidia-cudnn-cu12==9.17.1.4 ; sys_platform == 'linux'
+  - cuda-bindings==12.9.4 ; sys_platform == 'linux'
+  - nvidia-cuda-nvrtc-cu12==12.9.86 ; sys_platform == 'linux'
+  - nvidia-cuda-runtime-cu12==12.9.79 ; sys_platform == 'linux'
+  - nvidia-cuda-cupti-cu12==12.9.79 ; sys_platform == 'linux'
+  - nvidia-cudnn-cu12==9.10.2.21 ; sys_platform == 'linux'
+  - nvidia-cublas-cu12==12.9.1.4 ; sys_platform == 'linux'
+  - nvidia-cufft-cu12==11.4.1.4 ; sys_platform == 'linux'
+  - nvidia-curand-cu12==10.3.10.19 ; sys_platform == 'linux'
+  - nvidia-cusolver-cu12==11.7.5.82 ; sys_platform == 'linux'
+  - nvidia-cusparse-cu12==12.5.10.65 ; sys_platform == 'linux'
   - nvidia-cusparselt-cu12==0.7.1 ; sys_platform == 'linux'
-  - nvidia-nccl-cu12==2.28.9 ; sys_platform == 'linux'
+  - nvidia-nccl-cu12==2.27.5 ; sys_platform == 'linux'
   - nvidia-nvshmem-cu12==3.4.5 ; sys_platform == 'linux'
+  - nvidia-nvtx-cu12==12.9.79 ; sys_platform == 'linux'
+  - nvidia-nvjitlink-cu12==12.9.86 ; sys_platform == 'linux'
+  - nvidia-cufile-cu12==1.14.1.1 ; sys_platform == 'linux'
   - triton==3.6.0 ; sys_platform == 'linux'
   - optree>=0.13.0 ; extra == 'optree'
   - opt-einsum>=3.3 ; extra == 'opt-einsum'
   - pyyaml ; extra == 'pyyaml'
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-- pypi: https://download-r2.pytorch.org/whl/cu130/torch-2.11.0%2Bcu130-cp313-cp313-manylinux_2_28_aarch64.whl
+- pypi: https://download-r2.pytorch.org/whl/cu130/torch-2.10.0%2Bcu130-cp313-cp313-manylinux_2_28_aarch64.whl
   name: torch
-  version: 2.11.0+cu130
-  sha256: c3d60f79666b9101e3914a2e5dec2e81eac834e13cae0bcf59e94dc1a465f756
+  version: 2.10.0+cu130
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   requires_dist:
   - filelock
   - typing-extensions>=4.10.0
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   - jinja2
   - fsspec>=0.8.5
-  - cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==13.0.2 ; sys_platform == 'linux'
-  - cuda-bindings>=13.0.3,<14 ; sys_platform == 'linux'
-  - nvidia-cudnn-cu13==9.19.0.56 ; sys_platform == 'linux'
+  - cuda-bindings==13.0.3 ; sys_platform == 'linux'
+  - nvidia-cuda-nvrtc==13.0.88 ; sys_platform == 'linux'
+  - nvidia-cuda-runtime==13.0.96 ; sys_platform == 'linux'
+  - nvidia-cuda-cupti==13.0.85 ; sys_platform == 'linux'
+  - nvidia-cudnn-cu13==9.15.1.9 ; sys_platform == 'linux'
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   - nvidia-nccl-cu13==2.28.9 ; sys_platform == 'linux'
   - nvidia-nvshmem-cu13==3.4.5 ; sys_platform == 'linux'
+  - nvidia-nvtx==13.0.85 ; sys_platform == 'linux'
+  - nvidia-nvjitlink==13.0.88 ; sys_platform == 'linux'
+  - nvidia-cufile==1.15.1.6 ; sys_platform == 'linux'
   - triton==3.6.0 ; sys_platform == 'linux'
   - optree>=0.13.0 ; extra == 'optree'
   - opt-einsum>=3.3 ; extra == 'opt-einsum'
   - pyyaml ; extra == 'pyyaml'
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-- pypi: https://download-r2.pytorch.org/whl/cu130/torch-2.11.0%2Bcu130-cp313-cp313-manylinux_2_28_x86_64.whl
+- pypi: https://download-r2.pytorch.org/whl/cu130/torch-2.10.0%2Bcu130-cp313-cp313-manylinux_2_28_x86_64.whl
   name: torch
-  version: 2.11.0+cu130
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+  version: 2.10.0+cu130
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   requires_dist:
   - filelock
   - typing-extensions>=4.10.0
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   - networkx>=2.5.1
   - jinja2
   - fsspec>=0.8.5
-  - cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==13.0.2 ; sys_platform == 'linux'
-  - cuda-bindings>=13.0.3,<14 ; sys_platform == 'linux'
-  - nvidia-cudnn-cu13==9.19.0.56 ; sys_platform == 'linux'
+  - cuda-bindings==13.0.3 ; sys_platform == 'linux'
+  - nvidia-cuda-nvrtc==13.0.88 ; sys_platform == 'linux'
+  - nvidia-cuda-runtime==13.0.96 ; sys_platform == 'linux'
+  - nvidia-cuda-cupti==13.0.85 ; sys_platform == 'linux'
+  - nvidia-cudnn-cu13==9.15.1.9 ; sys_platform == 'linux'
+  - nvidia-cublas==13.1.0.3 ; sys_platform == 'linux'
+  - nvidia-cufft==12.0.0.61 ; sys_platform == 'linux'
+  - nvidia-curand==10.4.0.35 ; sys_platform == 'linux'
+  - nvidia-cusolver==12.0.4.66 ; sys_platform == 'linux'
+  - nvidia-cusparse==12.6.3.3 ; sys_platform == 'linux'
   - nvidia-cusparselt-cu13==0.8.0 ; sys_platform == 'linux'
   - nvidia-nccl-cu13==2.28.9 ; sys_platform == 'linux'
   - nvidia-nvshmem-cu13==3.4.5 ; sys_platform == 'linux'
+  - nvidia-nvtx==13.0.85 ; sys_platform == 'linux'
+  - nvidia-nvjitlink==13.0.88 ; sys_platform == 'linux'
+  - nvidia-cufile==1.15.1.6 ; sys_platform == 'linux'
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   - optree>=0.13.0 ; extra == 'optree'
   - opt-einsum>=3.3 ; extra == 'opt-einsum'
@@ -21296,6 +22666,22 @@ packages:
   - pandas ; extra == 'tutorials'
   - tabulate ; extra == 'tutorials'
   requires_python: '>=3.10,<3.15'
+- conda: https://conda.anaconda.org/conda-forge/noarch/typer-0.24.0-pyhcf101f3_0.conda
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+  - click >=8.2.1
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+  - shellingham >=1.3.0
+  - python
+  license: MIT
+  license_family: MIT
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 - conda: https://conda.anaconda.org/conda-forge/noarch/typer-0.25.0-pyhcf101f3_0.conda
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@@ -21357,6 +22743,18 @@ packages:
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   timestamp: 1777105465795
+- conda: https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhd8ed1ab_1.conda
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+  license: MIT
+  license_family: MIT
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+  size: 18923
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 - conda: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.15.0-pyhcf101f3_0.conda
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@@ -21376,6 +22774,17 @@ packages:
   purls: []
   size: 119135
   timestamp: 1767016325805
+- conda: https://conda.anaconda.org/conda-forge/noarch/ubiquerg-0.9.1-pyhd8ed1ab_0.conda
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+  md5: 5d3f41b0d51a614e1969e1cd5121d78d
+  depends:
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+  license: BSD-2-Clause
+  license_family: BSD
+  purls:
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+  size: 23166
+  timestamp: 1772256656029
 - conda: https://conda.anaconda.org/conda-forge/noarch/ubiquerg-0.9.3-pyhd8ed1ab_0.conda
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-- pypi: https://files.pythonhosted.org/packages/4b/eb/03bfb1299d4c4510329e470f13f9a4ce793df7fcb5a2fd3510f911066f61/virtualenv-21.3.0-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/c6/59/7d02447a55b2e55755011a647479041bc92a82e143f96a8195cb33bd0a1c/virtualenv-21.2.0-py3-none-any.whl
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+  version: 21.2.0
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   - importlib-metadata>=6.6 ; python_full_version < '3.8'
   - platformdirs>=3.9.1,<5
-  - python-discovery>=1.2.2
+  - python-discovery>=1
   - typing-extensions>=4.13.2 ; python_full_version < '3.11'
   requires_python: '>=3.8'
+- conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-21.2.0-pyhcf101f3_0.conda
+  sha256: b83246d145ba0e6814d2ed0b616293e56924e6c7d6649101f5a4f97f9e757ed1
+  md5: 704c22301912f7e37d0a92b2e7d5942d
+  depends:
+  - python >=3.10
+  - distlib >=0.3.7,<1
+  - filelock <4,>=3.24.2
+  - importlib-metadata >=6.6
+  - platformdirs >=3.9.1,<5
+  - python-discovery >=1
+  - typing_extensions >=4.13.2
+  - python
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/virtualenv?source=compressed-mapping
+  size: 4647775
+  timestamp: 1773133660203
 - conda: https://conda.anaconda.org/conda-forge/noarch/virtualenv-21.3.0-pyhcf101f3_0.conda
   sha256: defaf2bc2a3cf6f1455149531e8be4d03e18eb1d022ffe4f4d964d49bbf0fe34
   md5: da6e70a64226740cef159121dbe40b95
@@ -21506,17 +22933,17 @@ packages:
   - pkg:pypi/virtualenv?source=compressed-mapping
   size: 5161814
   timestamp: 1777321763628
-- pypi: https://files.pythonhosted.org/packages/56/af/400d84a3bdce0b062b4baa70acb6becd2c8018697f4fbf5af9a9e1e406e5/wandb-0.26.1-py3-none-manylinux_2_28_aarch64.whl
+- pypi: https://files.pythonhosted.org/packages/70/5f/81508581f0bb77b0495665c1c78e77606a48e66e855ca71ba7c8ae29efa4/wandb-0.25.1-py3-none-manylinux_2_28_aarch64.whl
   name: wandb
-  version: 0.26.1
-  sha256: 7c78bc2454cfe1ffa1c3a256060a387356eed8a4488e024d9d2eba8f2b5bd51d
+  version: 0.25.1
+  sha256: cc4521eb5223429ddab5e8eee9b42fdf4caabdf0bc4e0e809042720e5fbef0ed
   requires_dist:
   - click>=8.0.1
   - eval-type-backport ; python_full_version < '3.10'
   - gitpython>=1.0.0,!=3.1.29
   - packaging
   - platformdirs
-  - protobuf>4.21.0,!=5.28.0,!=5.29.0,<8
+  - protobuf>4.21.0,!=5.28.0,!=5.29.0,<7
   - pydantic<3
   - pyyaml
   - requests>=2.0.0,<3
@@ -21569,20 +22996,21 @@ packages:
   - rdkit ; extra == 'media'
   - soundfile ; extra == 'media'
   - cloudpickle ; extra == 'models'
+  - orjson ; extra == 'perf'
   - sweeps>=0.2.0 ; extra == 'sweeps'
   - wandb-workspaces ; extra == 'workspaces'
   requires_python: '>=3.9'
-- pypi: https://files.pythonhosted.org/packages/7b/e9/b4bf8f3509dcea1cec52233a38991459654635b5a8e6a494eb912e1b9cfb/wandb-0.26.1-py3-none-manylinux_2_28_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/f2/c7/445155ef010e2e35d190797d7c36ff441e062a5b566a6da4778e22233395/wandb-0.25.1-py3-none-manylinux_2_28_x86_64.whl
   name: wandb
-  version: 0.26.1
-  sha256: a2c8eeec8706dcd2872e69c3b4d20ec523082fdb4440295491556e219ad2aa67
+  version: 0.25.1
+  sha256: e73b4c55b947edae349232d5845204d30fac88e18eb4ad1d4b96bf7cf898405a
   requires_dist:
   - click>=8.0.1
   - eval-type-backport ; python_full_version < '3.10'
   - gitpython>=1.0.0,!=3.1.29
   - packaging
   - platformdirs
-  - protobuf>4.21.0,!=5.28.0,!=5.29.0,<8
+  - protobuf>4.21.0,!=5.28.0,!=5.29.0,<7
   - pydantic<3
   - pyyaml
   - requests>=2.0.0,<3
@@ -21635,9 +23063,45 @@ packages:
   - rdkit ; extra == 'media'
   - soundfile ; extra == 'media'
   - cloudpickle ; extra == 'models'
+  - orjson ; extra == 'perf'
   - sweeps>=0.2.0 ; extra == 'sweeps'
   - wandb-workspaces ; extra == 'workspaces'
   requires_python: '>=3.9'
+- conda: https://conda.anaconda.org/conda-forge/linux-64/wandb-0.25.1-py313h5c7d99a_0.conda
+  sha256: aba0006ba8bf50448688d5af9315cf111bc64ab170f6d99bc2c0b5b14138f050
+  md5: 4b73be0e0ef62a1fa4948c354c615d47
+  depends:
+  - __glibc >=2.17
+  - __glibc >=2.17,<3.0.a0
+  - appdirs >=1.4.3
+  - click >=8.0.1
+  - docker-pycreds >=0.4.0
+  - eval_type_backport
+  - gitpython >=1.0.0,!=3.1.29
+  - libgcc >=14
+  - packaging
+  - platformdirs
+  - protobuf >4.21.0,!=5.28.0,!=5.29.0,<7
+  - psutil >=5.0.0
+  - pydantic <3
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - pyyaml
+  - requests >=2.0.0,<3
+  - sentry-sdk >=2.0.0
+  - setproctitle
+  - setuptools
+  - six
+  - typing >=3.6.4
+  - typing_extensions >=4.8,<5
+  constrains:
+  - __glibc >=2.17
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/wandb?source=hash-mapping
+  size: 23209560
+  timestamp: 1773357586917
 - conda: https://conda.anaconda.org/conda-forge/linux-64/wandb-0.26.1-py313h843e2db_0.conda
   sha256: 9f78e8fd00bc7becb03257322a3b912bfbf60599226f6b508c2a967995c70afd
   md5: 68e9be6d6e2b4dbd092afe434a5ad6c0
@@ -21667,90 +23131,108 @@ packages:
   - pkg:pypi/wandb?source=hash-mapping
   size: 26459882
   timestamp: 1777056234676
-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/wandb-0.26.1-py313h5e7b836_0.conda
-  sha256: 495bdb94a8d64a89ebeda83b761d13ba22266e7750dc03d1763431a0d5754755
-  md5: 8d80457a768555a4401318f29bed4aa3
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/wandb-0.25.1-py313he77ad87_0.conda
+  sha256: 015e05f604c75d2f2da013da987e9a83d78c1acd8d8aa73d469c9ab8cfbd7f9d
+  md5: 30bfca510584243da0878a923602766d
   depends:
-  - packaging
-  - pydantic <3
-  - python
+  - appdirs >=1.4.3
   - click >=8.0.1
+  - docker-pycreds >=0.4.0
+  - eval_type_backport
   - gitpython >=1.0.0,!=3.1.29
+  - libgcc >=14
+  - packaging
   - platformdirs
-  - protobuf >4.21.0,!=5.28.0,!=5.29.0,<8
+  - protobuf >4.21.0,!=5.28.0,!=5.29.0,<7
+  - psutil >=5.0.0
+  - pydantic <3
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
   - pyyaml
   - requests >=2.0.0,<3
   - sentry-sdk >=2.0.0
-  - typing_extensions >=4.8,<5
+  - setproctitle
+  - setuptools
   - six
   - typing >=3.6.4
-  - python 3.13.* *_cp313
-  - libgcc >=14
-  - python_abi 3.13.* *_cp313
+  - typing_extensions >=4.8,<5
   constrains:
   - __glibc >=2.17
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/wandb?source=hash-mapping
-  size: 25273411
-  timestamp: 1777056254520
-- conda: https://conda.anaconda.org/conda-forge/osx-64/wandb-0.26.1-py313h23ec8f2_0.conda
-  sha256: 1f3acac1f00376cd181e64d10bb52a6ab86f600cd6dc544d10342f598369593d
-  md5: ce19115d535f08b0b91b30829301bbeb
-  depends:
-  - packaging
-  - pydantic <3
-  - python
+  size: 21404045
+  timestamp: 1773357603436
+- conda: https://conda.anaconda.org/conda-forge/osx-64/wandb-0.25.1-py313h7650d62_0.conda
+  sha256: 4e4aa8a998cb34242a93c4db633e19298a6361ccb6990bba4368a674f099512a
+  md5: 0cd585312b9f63c59d3d66c92c054f44
+  depends:
+  - __osx >=10.15
+  - appdirs >=1.4.3
   - click >=8.0.1
+  - docker-pycreds >=0.4.0
+  - eval_type_backport
   - gitpython >=1.0.0,!=3.1.29
+  - packaging
   - platformdirs
-  - protobuf >4.21.0,!=5.28.0,!=5.29.0,<8
+  - protobuf >4.21.0,!=5.28.0,!=5.29.0,<7
+  - psutil >=5.0.0
+  - pydantic <3
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
   - pyyaml
   - requests >=2.0.0,<3
   - sentry-sdk >=2.0.0
-  - typing_extensions >=4.8,<5
+  - setproctitle
+  - setuptools
   - six
   - typing >=3.6.4
-  - __osx >=11.0
-  - python_abi 3.13.* *_cp313
+  - typing_extensions >=4.8,<5
   constrains:
-  - __osx >=10.12
+  - __osx>=10.12
   - __osx >=10.13
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/wandb?source=hash-mapping
-  size: 25402031
-  timestamp: 1777056313825
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wandb-0.26.1-py313h212e517_0.conda
-  sha256: 83ade192556f98934bd2451d9caab00e672f116661f5c71f52687aeea4cd37d3
-  md5: 7a518260e3c25f4a077dc9716c5a1140
+  size: 22145235
+  timestamp: 1773358812752
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/wandb-0.25.1-py313h7d00576_0.conda
+  sha256: c5b6ff85c7ae6644baab04b1636a3f3251d4793efd73bd9d0ac3fca0b38fc0f1
+  md5: 6587cb310caae8740dd1488cd0f05feb
   depends:
-  - packaging
-  - pydantic <3
-  - python
+  - __osx >=11.0
+  - appdirs >=1.4.3
   - click >=8.0.1
+  - docker-pycreds >=0.4.0
+  - eval_type_backport
   - gitpython >=1.0.0,!=3.1.29
+  - packaging
   - platformdirs
-  - protobuf >4.21.0,!=5.28.0,!=5.29.0,<8
+  - protobuf >4.21.0,!=5.28.0,!=5.29.0,<7
+  - psutil >=5.0.0
+  - pydantic <3
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
   - pyyaml
   - requests >=2.0.0,<3
   - sentry-sdk >=2.0.0
-  - typing_extensions >=4.8,<5
+  - setproctitle
+  - setuptools
   - six
   - typing >=3.6.4
-  - python 3.13.* *_cp313
-  - __osx >=11.0
-  - python_abi 3.13.* *_cp313
+  - typing_extensions >=4.8,<5
   constrains:
   - __osx >=11.0
   license: MIT
   license_family: MIT
   purls:
   - pkg:pypi/wandb?source=hash-mapping
-  size: 24125979
-  timestamp: 1777056312977
+  size: 20809366
+  timestamp: 1773358379728
 - conda: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.14-pyhd8ed1ab_0.conda
   sha256: e311b64e46c6739e2a35ab8582c20fa30eb608da130625ed379f4467219d4813
   md5: 7e1e5ff31239f9cd5855714df8a3783d
@@ -22087,6 +23569,18 @@ packages:
   - pkg:pypi/yte?source=hash-mapping
   size: 16215
   timestamp: 1764250734338
+- conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.0-pyhcf101f3_1.conda
+  sha256: b4533f7d9efc976511a73ef7d4a2473406d7f4c750884be8e8620b0ce70f4dae
+  md5: 30cd29cb87d819caead4d55184c1d115
+  depends:
+  - python >=3.10
+  - python
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/zipp?source=hash-mapping
+  size: 24194
+  timestamp: 1764460141901
 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.1-pyhcf101f3_0.conda
   sha256: 523616c0530d305d2216c2b4a8dfd3872628b60083255b89c5e0d8c42e738cca
   md5: e1c36c6121a7c9c76f2f148f1e83b983
@@ -22096,7 +23590,7 @@ packages:
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/zipp?source=hash-mapping
+  - pkg:pypi/zipp?source=compressed-mapping
   size: 24461
   timestamp: 1776131454755
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.2-h25fd6f3_2.conda
diff --git a/pixi.toml b/pixi.toml
index c0837a366..5a9f89fb4 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -339,33 +339,40 @@ torch = { version = "*", index = "https://download.pytorch.org/whl/cu130" }
 # --- CuEquivariance
 # https://developer.nvidia.com/cuequivariance#section-get-started
 #
-# Problems (2026/01/13)
-# --------------------
+# Notes
+# -----
 # 1) Missing conda packages for cuequivariance kernels
 #    In CF we have cuequivariance and cuequivariance-torch
 #    At the moment the optimized kernels are not distributed in conda-forge
 #     * cuequivariance-ops-torch-cu12
-#   There are no sources and LICENSE does not allow to redistribute wheels, so not very CF friednly.
+#   There are no sources and LICENSE does not allow to redistribute wheels, so not very CF friendly.
 #   This install some other libs from pypi:
-#     * cuequivarance-ops-cu12
+#     * cuequivariance-ops-cu12
 #     * nvidia-cublas-cu12
 #   With cublas being the problematic one - also packaged in CF but not clobbering
 #   We would need to also package this in conda-forge
 #   See also: https://github.com/NVIDIA/cuEquivariance/issues/218
 #
-# 2) Tests fail with cuequivariance >=0.8, need to assess + pinning in the meantime
-# 2.1) No aarch wheel for cuequivariance-ops-torch-cu13 <0.8
-#
 
 [feature.cuequivariance-cuda12.target.linux-64.pypi-dependencies]
-cuequivariance = "<0.8"
-cuequivariance-torch = "<0.8"
-cuequivariance-ops-torch-cu12 = "<0.8"
+cuequivariance = ">=0.8"
+cuequivariance-torch = ">=0.8"
+cuequivariance-ops-torch-cu12 = ">=0.8"
+
+[feature.cuequivariance-cuda12.target.linux-aarch64.pypi-dependencies]
+cuequivariance = ">=0.8"
+cuequivariance-torch = ">=0.8"
+cuequivariance-ops-torch-cu12 = ">=0.8"
 
 [feature.cuequivariance-cuda13.target.linux-64.pypi-dependencies]
-cuequivariance = "<0.8"
-cuequivariance-torch = "<0.8"
-cuequivariance-ops-torch-cu13 = "<0.8"
+cuequivariance = ">=0.8"
+cuequivariance-torch = ">=0.8"
+cuequivariance-ops-torch-cu13 = ">=0.8"
+
+[feature.cuequivariance-cuda13.target.linux-aarch64.pypi-dependencies]
+cuequivariance = ">=0.8"
+cuequivariance-torch = ">=0.8"
+cuequivariance-ops-torch-cu13 = ">=0.8"
 
 # --- Tasks