Results from neuronxcc-2.28.7631.0+8f7b0cf5

README.md

@@ -5,7 +5,6 @@ tags:
 - neuron
 - trainium
 - transformers
-- sentence-transformers
 configs:
 - config_name: default
   data_files:
@@ -18,9 +17,8 @@ configs:
 Which Hugging Face Hub models run unmodified on AWS Trainium, through the
 native PyTorch backend
 [`torch-neuronx`](https://github.com/aws-neuron/torch-neuronx), for each
-`neuronx-cc` and library version tested. Tested libraries are
-[`transformers`](https://github.com/huggingface/transformers) and
-[`sentence-transformers`](https://github.com/huggingface/sentence-transformers).
+`neuronx-cc` and library version tested. The only library tested so far is
+[`transformers`](https://github.com/huggingface/transformers).
 
 To look up a model, use the
 [Space](https://huggingface.co/spaces/aws-neuron/torch-neuronx-model-support).
@@ -103,65 +101,6 @@ run 100 models, skipping those whose test script has not changed since their
 last run. Quantized checkpoints and models that need `trust_remote_code` are
 left out. A model that leaves the list keeps the results it already has.
 
-### sentence-transformers
-
-Each run scores one query against four documents using `SentenceTransformer`,
-`CrossEncoder`, or `MultiVectorEncoder`. SentenceTransformer encodes the query
-and documents and computes their similarity; CrossEncoder predicts a score for
-each query/document pair; MultiVectorEncoder compares token embeddings with
-MaxSim. Saved retrieval prompts and any model-specific task settings are recorded
-in the run script. All three paths use batch size 4 and the same input:
-
-```python
-query = "Which planet is known as the Red Planet?"
-documents = [
-    "Venus is often called Earth's twin because of its similar size and proximity.",
-    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
-    "Jupiter, the largest planet in our solar system, has a prominent red spot.",
-    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
-]
-```
-
-The model runs on CPU for reference, then on one logical Neuron device using
-native eager PyTorch. The optional compile sweep uses `model.compile()` with the
-Neuron backend for model forward calls; tokenization and similarity scoring
-remain outside that wrapper. It applies the same checks and records the mode and
-number of compiled graphs. Compile results are kept locally because the published
-schema does not distinguish execution modes.
-
-FP32 is the default, with BF16 for large checkpoints or
-following an allocation failure. SDPA is preferred, with eager attention retried
-when unsupported by the checkpoint. Each script records the checkpoint revision,
-Sentence Transformers source commit, compiler version, and final settings.
-Checkpoints that require custom code load with `trust_remote_code=True`.
-
-`PASS` requires four finite Neuron scores, Mars ranked first, CPU agreement at
-the same precision (`atol=rtol=0.001`), a stable repeat with the same tolerance,
-and a completed device-memory capture. `FAIL(output)` means a ranking, parity,
-or repeat check failed after execution. A ranking failure can also occur on CPU;
-it does not by itself indicate a Neuron defect. Loading and forward failures
-record their stage and traceback. Scores retain document order and are not
-comparable between models. This checks one retrieval example, not general
-retrieval quality or throughput.
-
-Memory is the highest observed sum of runtime HBM categories, sampled every
-100 ms from before model transfer until the final inference is synchronized.
-Capacity comes from the device properties. Values use GiB (bytes / 2**30),
-despite the Space's historical `Memory (GB)` label. Short peaks between samples
-may be missed; incomplete captures leave memory empty. Wall time includes
-imports, loading, the CPU reference, Neuron compilation and inference, the repeat,
-and monitoring. It is not per-query latency. `runner` names the encoder class;
-`neuron_cores=1` denotes one logical device, with no tensor parallelism.
-
-Selection takes the exact top 100 models from each Hub listing tagged
-`sentence-transformers`, by Trending and Downloads, and tests their deduplicated
-union. Sparse encoders, GGUF/MLX and quantized checkpoints, and unrelated
-classification tasks are excluded; excluded or failing entries are not replaced.
-The saved selection retains both ranks and checkpoint revisions. `selection_rank`
-is the Trending rank; Downloads ranks appear in notes and raw results. Scripts,
-logs, scores, memory samples, and prior attempts are saved under
-`validation/sentence-transformers/` in the dataset.
-
 ## Layout
 
 There is one file per Neuron target (`trn2`, `trn3`) and tested

data/trn3/neuronxcc-2.28.7631.0+8f7b0cf5/transformers-5.22.1.parquet

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