Card: long-format tables (model / attention / compiled); cornstack only

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---
base_model: nomic-ai/CodeRankEmbed
base_model_relation: quantized
library_name: sentence-transformers
license: mit
tags:
- flash-attention
- code-retrieval
- sentence-transformers
- nomic-bert
- bf16
language:
- en
---
# CodeRankEmbed-flash-attn

A **bf16 quantization of [`nomic-ai/CodeRankEmbed`](https://huggingface.co/nomic-ai/CodeRankEmbed)**
with a custom `modeling_hf_nomic_bert.py`, shipped in this repo, that **picks one of three
attention implementations for the hardware it runs on.** It is not a finetune — the weights are the
original CodeRankEmbed weights cast to bf16 (no further training). Two of the three replace the
original's padded `O(seq²)` attention with an `O(N)` unpadded path; the third keeps the original's
algorithm as the correctness reference and universal fallback.

## Why

`nomic-ai/CodeRankEmbed` loads through `trust_remote_code`, and its attention path is **eager
only** — activation memory grows as `batch × heads × seq²`, which runs out of memory at large
batches even though the model is only 137M params. This repo adds two attention paths that compute
the same attention in `O(N)` memory by packing unpadded sequences, so the large batches that the
eager path cannot fit run comfortably — with **parity embeddings** (no quality change):

- `torch_varlen` — `torch.nn.attention.varlen.varlen_attn`, shipped in torch itself (no extra
  package), available from torch **2.10.0** onward.
- `flash_attn` — the `flash_attn` package's varlen kernel. Kept as a fallback for older torch
  builds that don't yet have `torch.nn.attention.varlen` but do have `flash_attn` installed. This
  package is **optional**.

Both run the same FA2-family kernel and are gated by the same GPU-capability check. The modeling
file ships all three paths itself, so no runtime patching or post-load hooks are needed.

## Behavior

- **Three attention tiers, chosen automatically per device** (override with
  `NOMIC_BERT_ATTN_IMPL=torch_varlen|flash_attn|eager`):
  1. **`torch_varlen`** — CUDA, compute capability sm_80+ (Ampere or newer), torch **≥ 2.10.0**. No
     third-party kernel needed.
  2. **`flash_attn`** — CUDA, compute capability sm_80+, the `flash_attn` package importable, on a
     torch that doesn't yet ship `torch.nn.attention.varlen` (typically an older torch). This
     dependency is **optional**.
  3. **`eager`** — everything else: CPU, pre-Ampere GPUs, or neither of the above available. The
     original's padded attention algorithm, unchanged, runs on any host.
  `auto` (the default) prefers `torch_varlen`, then `flash_attn`, then `eager`. Before accepting a
  varlen tier, `auto` runs one tiny kernel probe per device (a capability check alone can't tell
  whether the installed build has a kernel for the GPU's architecture, e.g. on ROCm); if the probe
  raises a `RuntimeError` (how a missing or unsupported kernel fails), it logs one WARNING naming
  the tier and the error and steps down to the next tier. An out-of-memory error, any other
  exception type, or a CUDA error left over from earlier work propagates instead of demoting the
  tier, so a transient failure never pins the device to a slower tier. A forced override (e.g.
  `NOMIC_BERT_ATTN_IMPL=torch_varlen`) is not probed and **raises** `RuntimeError` if that tier's
  precondition doesn't hold — a forced tier never falls back silently. An unrecognized override
  raises `ValueError`.
- **See which tier engaged**: `model[0].auto_model.attention_impl` after a forward pass. The
  modeling file also logs the tier, and why, once at INFO.
- **Padding-free on the varlen tiers.** The model unpads the batch once, right after the input
  ids, runs the embeddings and every encoder layer on the real tokens only, and pads back once at
  the end. `last_hidden_state` keeps its `[B, S, 768]` shape, with zeros at padding positions, so
  pooling is unchanged.
- **Flattened batches (opt-in).** Loading with
  `model_kwargs={"attn_implementation": "flash_attention_2"}` lets sentence-transformers send each
  batch flattened (`[1, ΣL]` token ids with sequence boundaries, no padding at all), and the model
  returns one `[1, ΣL, 768]` output that sentence-transformers pools per sequence. The opt-in needs
  the `flash_attn` package installed, because transformers checks for it at load and raises
  otherwise, even though the attention itself still runs on whichever tier the model picks. On
  the `eager` tier the flattened batch is re-padded and runs the padded path.
- **Loads bf16 by default.** `flash_attn` and `torch_varlen` both require half precision and the
  model runs bf16 in any real serving setup, so the weights are stored bf16 and `config.json`
  declares `torch_dtype: bfloat16`. The original's custom `from_pretrained` silently dropped
  `torch_dtype` and always loaded fp32; the copy in this repo honors it, so the model loads bf16
  natively, like any normal HF model. Pass `torch_dtype=torch.float32` to load fp32 (note: the
  stored weights are bf16-precision, so this only widens the dtype, not the precision).
- **`eager` runs in bf16 too** (because the stored weights are bf16), numerically equivalent to the
  varlen tiers, just without their memory and throughput wins. The model loads and encodes on any
  host regardless of which tier engages.

## Usage

Identical to the original. The query prompt **must** include the task-instruction prefix
`"Represent this query for searching relevant code: "`; documents need no prefix.

```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("handwoven8588/CodeRankEmbed-flash-attn", trust_remote_code=True)
queries = ["Represent this query for searching relevant code: Calculate the n-th factorial"]
codes   = ["def fact(n):\n    if n < 0:\n        raise ValueError\n    return 1 if n == 0 else n * fact(n - 1)"]
q = model.encode(queries, normalize_embeddings=True)
d = model.encode(codes,   normalize_embeddings=True)
```

With `flash_attn` installed, opt in to flattened batches:

```python
model = SentenceTransformer(
    "handwoven8588/CodeRankEmbed-flash-attn",
    trust_remote_code=True,
    model_kwargs={"attn_implementation": "flash_attention_2"},
)
```

On a varlen tier the encoder layers run on packed tokens only, so they compile cleanly. For about
1.2× the throughput and a third less peak memory (see Speed and memory below):

```python
import torch
model[0].auto_model.encoder = torch.compile(model[0].auto_model.encoder, dynamic=True)
```

`dynamic=True` matters: the number of packed tokens changes with every batch.

## Parity & performance

Every number below compares this repo with the original model,
[`nomic-ai/CodeRankEmbed`](https://huggingface.co/nomic-ai/CodeRankEmbed) (revision `3c4b608`),
run as published: fp32 weights, its own modeling file, padded eager attention. Both run on the
same GPU, an RTX 3090 Ti (sm_86). The original's modeling file calls
`get_extended_attention_mask`, which transformers has deprecated and no longer ships by 5.17, so
it was measured on transformers 5.11; this repo does not call it and loads on 5.17 too.

**Corpus.** The first 16,384 `document` fields (real Python functions) of
[`lightonai/cornstack`](https://huggingface.co/datasets/lightonai/cornstack)'s Python split, in
shard order (`train-00000-of-00682.parquet`, then `train-00001-…`): 2,684,878 tokens, 7 to 4,746
per function, 164 mean, 92 median. Each row is one `encode()` call over all of them at the stated
`batch_size=`, after one untimed warm-up of the same call. Cosine similarity is per function, on
fp32-renormalized output, against the original's embedding of the same function. Peak memory is
the encode's peak CUDA allocation above the loaded weights. **Load** is how the model was loaded:
`default` (as in Usage) or `flattened` (the opt-in above). `torch 2.12.1+cu130`,
`transformers 5.11.0`, `sentence-transformers 6.1.0`, `flash-attn 2.8.3.post1`.

### Agreement with the original model

| model | attention | compiled | load | batch size | min cosine | mean cosine |
| --- | --- | --- | --- | --- | --- | --- |
| `nomic-ai/CodeRankEmbed` | `eager` | – | default | 4 | 1 (the reference) | 1 (the reference) |
| `handwoven8588/CodeRankEmbed-flash-attn` | `eager` | – | default | 4 | 0.99902 | 0.99991 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `eager` | – | flattened | 4 | 0.99916 | 0.99991 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | default | 256 | 0.99896 | 0.99993 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | flattened | 256 | 0.99896 | 0.99993 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `flash_attn` | – | default | 256 | 0.99841 | 0.99993 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `flash_attn` | – | flattened | 256 | 0.99841 | 0.99993 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | default | 256 | 0.99960 | 0.99995 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | flattened | 256 | 0.99960 | 0.99995 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `flash_attn` | ✓ | default | 256 | 0.99946 | 0.99995 |
| `handwoven8588/CodeRankEmbed-flash-attn` | `flash_attn` | ✓ | flattened | 256 | 0.99946 | 0.99995 |

The original is the reference every other row is measured against, so its cosine is 1 by
definition, not a measurement. The mean cosine is 0.99991 or higher in every configuration; the
minimum is the one function, of 16,384, that moves most under bf16 weights and arithmetic. The
batch size does not change these figures (a varlen tier gives the same five decimals at batch 32
and 256), so the largest batch that ran in every load is shown; `eager` runs at batch 4 (see
below). Compiled rows sit closer to the original than uncompiled ones.

### Speed and memory

| model | attention | compiled | load | batch size | peak memory | time |
| --- | --- | --- | --- | --- | --- | --- |
| `nomic-ai/CodeRankEmbed` | `eager` | – | default | 4 | 8.4 GiB | 68.9 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | default | 32 | 2.5 GiB | 16.3 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | default | 256 | 10.9 GiB | 15.1 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | default | 1,024 | out of memory | out of memory |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | default | 2,048 | out of memory | out of memory |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | flattened | 32 | 1.4 GiB | 15.5 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | flattened | 256 | 6.9 GiB | 13.6 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | flattened | 1,024 | 17.0 GiB | 13.4 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | – | flattened | 2,048 | out of memory | out of memory |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | default | 32 | 1.6 GiB | 14.0 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | default | 256 | 7.1 GiB | 12.8 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | default | 1,024 | 16.3 GiB | 13.8 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | default | 2,048 | out of memory | out of memory |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | flattened | 32 | 0.9 GiB | 13.2 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | flattened | 256 | 4.5 GiB | 11.4 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | flattened | 1,024 | 11.1 GiB | 10.9 s |
| `handwoven8588/CodeRankEmbed-flash-attn` | `torch_varlen` | ✓ | flattened | 2,048 | 16.6 GiB | 10.8 s |

This repo's rows are `torch_varlen`; `flash_attn` runs within 1% of it in both time and memory
at every cell. The original runs at batch 4: its padded fp32 attention needs
`batch × heads × seq²` memory, 8.4 GiB at batch 4 for this corpus's longest function. The same
algorithm in bf16 (this repo's `eager` tier, batch 4) takes 41.2 s in 4.2 GiB; the rest of the
gain comes from the unpadded path and the batches it allows. Compiled means
`model[0].auto_model.encoder = torch.compile(model[0].auto_model.encoder, dynamic=True)`.

- **Against the original**, the same 16,384 functions encode 5.1× faster uncompiled (13.4 s,
  flattened at batch 1,024) and 6.4× faster compiled (10.8 s, flattened at batch 2,048), against
  68.9 s. At batch 32 this repo needs 2.5 GiB (default load) or 1.4 GiB (flattened), against the
  original's 8.4 GiB at batch 4.
- **Memory follows the real tokens in the heaviest batch, not the number of functions.** This
  repo runs the encoder on real tokens only, and every out-of-memory cell failed allocating one
  activation over that batch's tokens: the 3,072-wide MLP hidden state, or, compiled, the
  2,304-wide QKV projection. sentence-transformers length-sorts the corpus, so the default load's
  first batch of 1,024 holds the longest functions, about 870,000 tokens; flattened, it pairs the
  longest with the shortest, so its heaviest batch of 1,024 holds about 594,000.
- **Flattening** cuts peak memory by 32–42% against the default load at the same batch size, and
  wall time by 5–21% (more at larger batches).
- **Compiling** is 1.17–1.23× faster at the same batch size and cuts peak memory by 35%. It
  compiles one graph, with no graph breaks, once per process (the first encode takes about 5 s
  longer), and does not recompile as batch lengths change; the only further compiles come once a
  batch passes about 699,000 and 932,000 tokens, where inductor switches to 64-bit indexing for
  the 3,072-wide MLP activation and the 2,304-wide QKV projection.

## What changed vs the source repo

1. **Weights**: fp32 → bf16. `flash_attn` and `torch_varlen` only accept half precision and the
   model runs bf16 in any real serving configuration, so the weights are stored bf16 and (via the
   load fix below) arrive bf16 — which is simply how this model is used, and removes the need for a
   post-load dtype cast. Parity-neutral; the smaller download is incidental, not the reason.
2. **`from_pretrained` dtype fix**: the original's custom `from_pretrained` instantiated the model
   fp32 and `load_state_dict`-ed the checkpoint into fp32 params, **ignoring `torch_dtype`**. The
   copy here adds the standard transformers dtype resolution (explicit arg → `config.torch_dtype` →
   checkpoint dtype) so the model loads in its declared dtype.
3. **Three attention tiers**: `NomicBertAttention.forward` now selects one of three attention
   implementations at call time — `torch_varlen` (torch's own
   `torch.nn.attention.varlen.varlen_attn`, no third-party kernel, needs torch ≥ 2.10.0),
   `flash_attn` (the `flash_attn` package's varlen kernel, kept as a fallback for older torch
   builds that have it installed), and `eager` (the original's padded attention algorithm,
   numerically unchanged, and the default off CUDA sm_80+; it now converts a raw 2-D `[B, S]` mask
   to the additive form before adding it, for the case where a `device_map` split hands it a mask
   a varlen tier upstream never passes). On both varlen tiers `NomicBertModel.forward` unpads the
   batch once with torch-native `_unpad`/`_pad` helpers (a replacement for
   `flash_attn.bert_padding`), keeps the hidden states packed `[ΣL, 768]` through the embeddings
   and every layer, and pads back once at the end. Rotary embeddings rotate each packed token by
   its position within its own sequence, the same rotation it gets in the padded batch. On the
   eager tier `NomicBertModel.forward` builds the additive attention mask inline instead of calling
   the (now-removed-upstream) `get_extended_attention_mask` helper. Set
   `NOMIC_BERT_ATTN_IMPL=torch_varlen|flash_attn|eager` to force a tier (raises if it can't
   engage); `model[0].auto_model.attention_impl` and the one-time INFO log line report which tier
   engaged.
4. **`from_pretrained` forwards the Hub revision**: the original's custom `from_pretrained`
   fetched the weight file without the caller's `revision` (and cache/token options), so a pinned
   load still took the weights from the default branch. The copy here passes them through, so a
   pinned load fetches the weights from the pinned commit too. It also accepts transformers'
   `dtype=` alongside the older `torch_dtype=`, and returns the model in eval mode, as
   transformers' own `from_pretrained` does.
5. **Flattened input**: `NomicBertModel.forward` also accepts sentence-transformers' flattened
   batches (`input_ids [1, ΣL]` with per-sequence `position_ids` and `cu_seq_lens_q`), and the
   model declares flash-attention support so that sentence-transformers sends them when loaded
   with `attn_implementation="flash_attention_2"` (see Behavior).

## License & attribution

MIT — same license as `nomic-ai/CodeRankEmbed` (see `NOTICE`). The weights, tokenizer, and the bulk
of the modeling file are a verbatim derivative of `nomic-ai/CodeRankEmbed`; the modeling file
derives from Tri Dao's BERT implementation, and `CodeRankEmbed` was trained by the CoRNStack team
(Suresh et al., 2025). Cite their work:

```bibtex
@misc{suresh2025cornstackhighqualitycontrastivedata,
      title={CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking},
      author={Tarun Suresh and Revanth Gangi Reddy and Yifei Xu and Zach Nussbaum and Andriy Mulyar and Brandon Duderstadt and Heng Ji},
      year={2025},
      eprint={2412.01007},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.01007},
}
```

	---
	base_model: nomic-ai/CodeRankEmbed
	base_model_relation: quantized
	library_name: sentence-transformers
	license: mit
	tags:
	- flash-attention
	- code-retrieval
	- sentence-transformers
	- nomic-bert
	- bf16
	language:
	- en
	---

	# CodeRankEmbed-flash-attn

	A bf16 quantization of [`nomic-ai/CodeRankEmbed`](https://huggingface.co/nomic-ai/CodeRankEmbed)
	with a custom `modeling_hf_nomic_bert.py`, shipped in this repo, that **picks one of three
	attention implementations for the hardware it runs on.** It is not a finetune — the weights are the
	original CodeRankEmbed weights cast to bf16 (no further training). Two of the three replace the
	original's padded `O(seq²)` attention with an `O(N)` unpadded path; the third keeps the original's
	algorithm as the correctness reference and universal fallback.

	## Why

	`nomic-ai/CodeRankEmbed` loads through `trust_remote_code`, and its attention path is **eager
	only** — activation memory grows as `batch × heads × seq²`, which runs out of memory at large
	batches even though the model is only 137M params. This repo adds two attention paths that compute
	the same attention in `O(N)` memory by packing unpadded sequences, so the large batches that the
	eager path cannot fit run comfortably — with parity embeddings (no quality change):

	- `torch_varlen` — `torch.nn.attention.varlen.varlen_attn`, shipped in torch itself (no extra
	package), available from torch 2.10.0 onward.
	- `flash_attn` — the `flash_attn` package's varlen kernel. Kept as a fallback for older torch
	builds that don't yet have `torch.nn.attention.varlen` but do have `flash_attn` installed. This
	package is optional.

	Both run the same FA2-family kernel and are gated by the same GPU-capability check. The modeling
	file ships all three paths itself, so no runtime patching or post-load hooks are needed.

	## Behavior

	- Three attention tiers, chosen automatically per device (override with
	`NOMIC_BERT_ATTN_IMPL=torch_varlen\|flash_attn\|eager`):
	1. `torch_varlen` — CUDA, compute capability sm_80+ (Ampere or newer), torch ≥ 2.10.0. No
	third-party kernel needed.
	2. `flash_attn` — CUDA, compute capability sm_80+, the `flash_attn` package importable, on a
	torch that doesn't yet ship `torch.nn.attention.varlen` (typically an older torch). This
	dependency is optional.
	3. `eager` — everything else: CPU, pre-Ampere GPUs, or neither of the above available. The
	original's padded attention algorithm, unchanged, runs on any host.

	`auto` (the default) prefers `torch_varlen`, then `flash_attn`, then `eager`. Before accepting a
	varlen tier, `auto` runs one tiny kernel probe per device (a capability check alone can't tell
	whether the installed build has a kernel for the GPU's architecture, e.g. on ROCm); if the probe
	raises a `RuntimeError` (how a missing or unsupported kernel fails), it logs one WARNING naming
	the tier and the error and steps down to the next tier. An out-of-memory error, any other
	exception type, or a CUDA error left over from earlier work propagates instead of demoting the
	tier, so a transient failure never pins the device to a slower tier. A forced override (e.g.
	`NOMIC_BERT_ATTN_IMPL=torch_varlen`) is not probed and raises `RuntimeError` if that tier's
	precondition doesn't hold — a forced tier never falls back silently. An unrecognized override
	raises `ValueError`.
	- See which tier engaged: `model[0].auto_model.attention_impl` after a forward pass. The
	modeling file also logs the tier, and why, once at INFO.
	- Padding-free on the varlen tiers. The model unpads the batch once, right after the input
	ids, runs the embeddings and every encoder layer on the real tokens only, and pads back once at
	the end. `last_hidden_state` keeps its `[B, S, 768]` shape, with zeros at padding positions, so
	pooling is unchanged.
	- Flattened batches (opt-in). Loading with
	`model_kwargs={"attn_implementation": "flash_attention_2"}` lets sentence-transformers send each
	batch flattened (`[1, ΣL]` token ids with sequence boundaries, no padding at all), and the model
	returns one `[1, ΣL, 768]` output that sentence-transformers pools per sequence. The opt-in needs
	the `flash_attn` package installed, because transformers checks for it at load and raises
	otherwise, even though the attention itself still runs on whichever tier the model picks. On
	the `eager` tier the flattened batch is re-padded and runs the padded path.
	- Loads bf16 by default. `flash_attn` and `torch_varlen` both require half precision and the
	model runs bf16 in any real serving setup, so the weights are stored bf16 and `config.json`
	declares `torch_dtype: bfloat16`. The original's custom `from_pretrained` silently dropped
	`torch_dtype` and always loaded fp32; the copy in this repo honors it, so the model loads bf16
	natively, like any normal HF model. Pass `torch_dtype=torch.float32` to load fp32 (note: the
	stored weights are bf16-precision, so this only widens the dtype, not the precision).
	- `eager` runs in bf16 too (because the stored weights are bf16), numerically equivalent to the
	varlen tiers, just without their memory and throughput wins. The model loads and encodes on any
	host regardless of which tier engages.

	## Usage

	Identical to the original. The query prompt must include the task-instruction prefix
	`"Represent this query for searching relevant code: "`; documents need no prefix.

	```python
	from sentence_transformers import SentenceTransformer

	model = SentenceTransformer("handwoven8588/CodeRankEmbed-flash-attn", trust_remote_code=True)
	queries = ["Represent this query for searching relevant code: Calculate the n-th factorial"]
	codes = ["def fact(n):\n if n < 0:\n raise ValueError\n return 1 if n == 0 else n * fact(n - 1)"]

	q = model.encode(queries, normalize_embeddings=True)
	d = model.encode(codes, normalize_embeddings=True)
	```

	With `flash_attn` installed, opt in to flattened batches:

	```python
	model = SentenceTransformer(
	"handwoven8588/CodeRankEmbed-flash-attn",
	trust_remote_code=True,
	model_kwargs={"attn_implementation": "flash_attention_2"},
	)
	```

	On a varlen tier the encoder layers run on packed tokens only, so they compile cleanly. For about
	1.2× the throughput and a third less peak memory (see Speed and memory below):

	```python
	import torch

	model[0].auto_model.encoder = torch.compile(model[0].auto_model.encoder, dynamic=True)
	```

	`dynamic=True` matters: the number of packed tokens changes with every batch.

	## Parity & performance

	Every number below compares this repo with the original model,
	[`nomic-ai/CodeRankEmbed`](https://huggingface.co/nomic-ai/CodeRankEmbed) (revision `3c4b608`),
	run as published: fp32 weights, its own modeling file, padded eager attention. Both run on the
	same GPU, an RTX 3090 Ti (sm_86). The original's modeling file calls
	`get_extended_attention_mask`, which transformers has deprecated and no longer ships by 5.17, so
	it was measured on transformers 5.11; this repo does not call it and loads on 5.17 too.

	Corpus. The first 16,384 `document` fields (real Python functions) of
	[`lightonai/cornstack`](https://huggingface.co/datasets/lightonai/cornstack)'s Python split, in
	shard order (`train-00000-of-00682.parquet`, then `train-00001-…`): 2,684,878 tokens, 7 to 4,746
	per function, 164 mean, 92 median. Each row is one `encode()` call over all of them at the stated
	`batch_size=`, after one untimed warm-up of the same call. Cosine similarity is per function, on
	fp32-renormalized output, against the original's embedding of the same function. Peak memory is
	the encode's peak CUDA allocation above the loaded weights. Load is how the model was loaded:
	`default` (as in Usage) or `flattened` (the opt-in above). `torch 2.12.1+cu130`,
	`transformers 5.11.0`, `sentence-transformers 6.1.0`, `flash-attn 2.8.3.post1`.

	### Agreement with the original model

	\| model \| attention \| compiled \| load \| batch size \| min cosine \| mean cosine \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| `nomic-ai/CodeRankEmbed` \| `eager` \| – \| default \| 4 \| 1 (the reference) \| 1 (the reference) \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `eager` \| – \| default \| 4 \| 0.99902 \| 0.99991 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `eager` \| – \| flattened \| 4 \| 0.99916 \| 0.99991 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| default \| 256 \| 0.99896 \| 0.99993 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| flattened \| 256 \| 0.99896 \| 0.99993 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `flash_attn` \| – \| default \| 256 \| 0.99841 \| 0.99993 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `flash_attn` \| – \| flattened \| 256 \| 0.99841 \| 0.99993 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| default \| 256 \| 0.99960 \| 0.99995 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| flattened \| 256 \| 0.99960 \| 0.99995 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `flash_attn` \| ✓ \| default \| 256 \| 0.99946 \| 0.99995 \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `flash_attn` \| ✓ \| flattened \| 256 \| 0.99946 \| 0.99995 \|

	The original is the reference every other row is measured against, so its cosine is 1 by
	definition, not a measurement. The mean cosine is 0.99991 or higher in every configuration; the
	minimum is the one function, of 16,384, that moves most under bf16 weights and arithmetic. The
	batch size does not change these figures (a varlen tier gives the same five decimals at batch 32
	and 256), so the largest batch that ran in every load is shown; `eager` runs at batch 4 (see
	below). Compiled rows sit closer to the original than uncompiled ones.

	### Speed and memory

	\| model \| attention \| compiled \| load \| batch size \| peak memory \| time \|
	\| --- \| --- \| --- \| --- \| --- \| --- \| --- \|
	\| `nomic-ai/CodeRankEmbed` \| `eager` \| – \| default \| 4 \| 8.4 GiB \| 68.9 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| default \| 32 \| 2.5 GiB \| 16.3 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| default \| 256 \| 10.9 GiB \| 15.1 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| default \| 1,024 \| out of memory \| out of memory \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| default \| 2,048 \| out of memory \| out of memory \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| flattened \| 32 \| 1.4 GiB \| 15.5 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| flattened \| 256 \| 6.9 GiB \| 13.6 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| flattened \| 1,024 \| 17.0 GiB \| 13.4 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| – \| flattened \| 2,048 \| out of memory \| out of memory \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| default \| 32 \| 1.6 GiB \| 14.0 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| default \| 256 \| 7.1 GiB \| 12.8 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| default \| 1,024 \| 16.3 GiB \| 13.8 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| default \| 2,048 \| out of memory \| out of memory \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| flattened \| 32 \| 0.9 GiB \| 13.2 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| flattened \| 256 \| 4.5 GiB \| 11.4 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| flattened \| 1,024 \| 11.1 GiB \| 10.9 s \|
	\| `handwoven8588/CodeRankEmbed-flash-attn` \| `torch_varlen` \| ✓ \| flattened \| 2,048 \| 16.6 GiB \| 10.8 s \|

	This repo's rows are `torch_varlen`; `flash_attn` runs within 1% of it in both time and memory
	at every cell. The original runs at batch 4: its padded fp32 attention needs
	`batch × heads × seq²` memory, 8.4 GiB at batch 4 for this corpus's longest function. The same
	algorithm in bf16 (this repo's `eager` tier, batch 4) takes 41.2 s in 4.2 GiB; the rest of the
	gain comes from the unpadded path and the batches it allows. Compiled means
	`model[0].auto_model.encoder = torch.compile(model[0].auto_model.encoder, dynamic=True)`.

	- Against the original, the same 16,384 functions encode 5.1× faster uncompiled (13.4 s,
	flattened at batch 1,024) and 6.4× faster compiled (10.8 s, flattened at batch 2,048), against
	68.9 s. At batch 32 this repo needs 2.5 GiB (default load) or 1.4 GiB (flattened), against the
	original's 8.4 GiB at batch 4.
	- Memory follows the real tokens in the heaviest batch, not the number of functions. This
	repo runs the encoder on real tokens only, and every out-of-memory cell failed allocating one
	activation over that batch's tokens: the 3,072-wide MLP hidden state, or, compiled, the
	2,304-wide QKV projection. sentence-transformers length-sorts the corpus, so the default load's
	first batch of 1,024 holds the longest functions, about 870,000 tokens; flattened, it pairs the
	longest with the shortest, so its heaviest batch of 1,024 holds about 594,000.
	- Flattening cuts peak memory by 32–42% against the default load at the same batch size, and
	wall time by 5–21% (more at larger batches).
	- Compiling is 1.17–1.23× faster at the same batch size and cuts peak memory by 35%. It
	compiles one graph, with no graph breaks, once per process (the first encode takes about 5 s
	longer), and does not recompile as batch lengths change; the only further compiles come once a
	batch passes about 699,000 and 932,000 tokens, where inductor switches to 64-bit indexing for
	the 3,072-wide MLP activation and the 2,304-wide QKV projection.

	## What changed vs the source repo

	1. Weights: fp32 → bf16. `flash_attn` and `torch_varlen` only accept half precision and the
	model runs bf16 in any real serving configuration, so the weights are stored bf16 and (via the
	load fix below) arrive bf16 — which is simply how this model is used, and removes the need for a
	post-load dtype cast. Parity-neutral; the smaller download is incidental, not the reason.
	2. `from_pretrained` dtype fix: the original's custom `from_pretrained` instantiated the model
	fp32 and `load_state_dict`-ed the checkpoint into fp32 params, ignoring `torch_dtype`. The
	copy here adds the standard transformers dtype resolution (explicit arg → `config.torch_dtype` →
	checkpoint dtype) so the model loads in its declared dtype.
	3. Three attention tiers: `NomicBertAttention.forward` now selects one of three attention
	implementations at call time — `torch_varlen` (torch's own
	`torch.nn.attention.varlen.varlen_attn`, no third-party kernel, needs torch ≥ 2.10.0),
	`flash_attn` (the `flash_attn` package's varlen kernel, kept as a fallback for older torch
	builds that have it installed), and `eager` (the original's padded attention algorithm,
	numerically unchanged, and the default off CUDA sm_80+; it now converts a raw 2-D `[B, S]` mask
	to the additive form before adding it, for the case where a `device_map` split hands it a mask
	a varlen tier upstream never passes). On both varlen tiers `NomicBertModel.forward` unpads the
	batch once with torch-native `_unpad`/`_pad` helpers (a replacement for
	`flash_attn.bert_padding`), keeps the hidden states packed `[ΣL, 768]` through the embeddings
	and every layer, and pads back once at the end. Rotary embeddings rotate each packed token by
	its position within its own sequence, the same rotation it gets in the padded batch. On the
	eager tier `NomicBertModel.forward` builds the additive attention mask inline instead of calling
	the (now-removed-upstream) `get_extended_attention_mask` helper. Set
	`NOMIC_BERT_ATTN_IMPL=torch_varlen\|flash_attn\|eager` to force a tier (raises if it can't
	engage); `model[0].auto_model.attention_impl` and the one-time INFO log line report which tier
	engaged.
	4. `from_pretrained` forwards the Hub revision: the original's custom `from_pretrained`
	fetched the weight file without the caller's `revision` (and cache/token options), so a pinned
	load still took the weights from the default branch. The copy here passes them through, so a
	pinned load fetches the weights from the pinned commit too. It also accepts transformers'
	`dtype=` alongside the older `torch_dtype=`, and returns the model in eval mode, as
	transformers' own `from_pretrained` does.
	5. Flattened input: `NomicBertModel.forward` also accepts sentence-transformers' flattened
	batches (`input_ids [1, ΣL]` with per-sequence `position_ids` and `cu_seq_lens_q`), and the
	model declares flash-attention support so that sentence-transformers sends them when loaded
	with `attn_implementation="flash_attention_2"` (see Behavior).

	## License & attribution

	MIT — same license as `nomic-ai/CodeRankEmbed` (see `NOTICE`). The weights, tokenizer, and the bulk
	of the modeling file are a verbatim derivative of `nomic-ai/CodeRankEmbed`; the modeling file
	derives from Tri Dao's BERT implementation, and `CodeRankEmbed` was trained by the CoRNStack team
	(Suresh et al., 2025). Cite their work:

	```bibtex
	@misc{suresh2025cornstackhighqualitycontrastivedata,
	title={CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking},
	author={Tarun Suresh and Revanth Gangi Reddy and Yifei Xu and Zach Nussbaum and Andriy Mulyar and Brandon Duderstadt and Heng Ji},
	year={2025},
	eprint={2412.01007},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2412.01007},
	}
	```