# CodeRankEmbed-flash-attn by Jackson Davis: Open-Weight Model Source: https://savrn.com/models/coderankembed-flash-attn Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve CodeRankEmbed-flash-attn (137M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.3 GB | 0.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 7, 2026. [CodeRankEmbed-flash-attn on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/coderankembed-flash-attn/gpus) ## Model Card By Jackson Davis, published under mit, revision 8187d6a65fa3. A bf16 quantization of [nomic-ai/CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) with a three-tier attention dispatch built into a custom modeling_hf_nomic_bert.py shipped in this repo. It is not a finetune — the weights are the original CodeRankEmbed weights cast to bf16 (no further training). Two of the three tiers replace the original eager O(seq²) attention with an O(N) unpadded path; the third keeps the original eager 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 OOMs 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 OOM the eager path run comfortably — with parity embeddings (no quality change): [Read the full model card (1,252 words)](https://savrn.com/models/coderankembed-flash-attn/card) ## Configuration Architecture NomicBertModel Vocabulary size 30,528 Stored precision bfloat16 Model type nomic_bert ## Identity and Version Repository handwoven8588/CodeRankEmbed-flash-attn Publisher Jackson Davis Task Not stated by the source Modality Other Library sentence-transformers Parameters 137M parameters Languages en Revision 8187d6a65fa30b4d79004eebf7ca63977eed003a First published 2026-06-20 Last updated 2026-09-26 ## Files and Weights 15 files, 274.5 MB in total. The weights are 1 file totalling 273.5 MB in safetensors. Weights1 file · 273.5 MB Configuration8 files · 69.4 KB Tokenizer3 files · 944.6 KB Documentation2 files · 10.7 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 273.5 MB | 919b7d8b4352 | | 1_Pooling/config.json | Configuration | 296 B | — | | config.json | Configuration | 1.5 KB | — | | config_sentence_transformers.json | Configuration | 245 B | — | | configuration_hf_nomic_bert.py | Configuration | 2.0 KB | — | | modeling_hf_nomic_bert.py | Configuration | 64.3 KB | — | | modules.json | Configuration | 229 B | — | | sentence_bert_config.json | Configuration | 140 B | — | | special_tokens_map.json | Configuration | 695 B | — | | NOTICE | Documentation | 1.3 KB | — | | README.md | Documentation | 9.4 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 711.6 KB | — | | tokenizer_config.json | Tokenizer | 1.4 KB | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License mit Access Open weights, no gate Download size 273.5 MB [Download from Jackson Davis](https://huggingface.co/handwoven8588/CodeRankEmbed-flash-attn) Released by Jackson Davis through its official repository on Hugging Face. [Read the license](https://opensource.org/license/mit). ## Built From - Derived from nomic-ai/CodeRankEmbed - Quantized from nomic-ai/CodeRankEmbed ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 273.5 MB | | 16-bit | 0.3 GB | | 8-bit | 0.1 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About CodeRankEmbed-flash-attn ### How much GPU memory does CodeRankEmbed-flash-attn need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (137M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run CodeRankEmbed-flash-attn on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use CodeRankEmbed-flash-attn commercially? Yes. CodeRankEmbed-flash-attn is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included. ## Jackson Davis [All models and datasets](https://savrn.com/model-publishers/handwoven8588) ## Versions - [8187d6a65fa3](https://savrn.com/models/coderankembed-flash-attn/versions/8187d6a65fa3) · current 2026-09-26 ## Explore More - [All models under mit](https://savrn.com/models/licenses/mit) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-09-26. - [Hugging Face record](https://huggingface.co/handwoven8588/CodeRankEmbed-flash-attn) - [How the hub is built](https://savrn.com/model-hub/methodology)