codesage-large-v2

Read the disclaimer below before using this model.

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codesage-large-v2 -- ONNX for Teradata BYOM

This repository hosts an ONNX-converted version of the upstream model codesage/codesage-large-v2, packaged for the Teradata Vantage mldb.ONNXEmbeddings BYOM function. It is not the original PyTorch model -- only the inference graph and tokenizer needed for in-database embedding generation.

What's different from upstream:

• Format: ONNX (opset 14, IR version 8 -- BYOM 6+ compatible), produced from the upstream weights with architecture-aware post-processing baked in.
• Precision: dynamic int8 quantization. See the variants table below for what is shipped for this model.
• Pooling and post-processing: this graph emits the raw sentence_embedding tensor. Pooling rule is mean.
• Verification: every variant's cosine fidelity vs. the upstream PyTorch reference is recorded on a fixed CodeSearchNet sample. Numbers may not generalize to your data.

Model details

<details> <summary>Full language list (9)</summary>

• c
• c-sharp
• go
• java
• javascript
• typescript
• php
• python
• ruby

</details>

Quantization variants

This repository ships the following variants. Quality numbers are measured against the upstream PyTorch reference on a fixed CodeSearchNet sample. The Size column is the on-disk size of the ONNX weight file in megabytes (MB, 10^6 bytes).

How to read the quality columns:

• p50 cosine is the median cosine similarity between this variant's embeddings and the fp32 ONNX reference, computed over a fixed evaluation set. Higher means closer to the unquantized model; 1.0 is identical.
• R@1 is top-1 retrieval consistency: if you use this variant as a search index, R@1 is the fraction of queries that get the same nearest neighbor as the fp32 reference would. Higher is better.

Notes:

• ffn_skip: dynamic int8 with the feed-forward (FFN) MatMul layers kept in fp32, while attention and projection MatMuls stay quantized. The FFN layers are where most of the quantization error in transformer blocks concentrates; leaving them in fp32 recovers most of the quality loss for a modest size increase. The artifact is roughly 3x smaller than fp32 (larger than the per_channel int8 sibling).

Quickstart: using this model with Teradata BYOM

Requires Teradata Vantage with BYOM 6+ (mldb.ONNXEmbeddings).

import getpass
import teradataml as tdml
from huggingface_hub import hf_hub_download

repo_id   = "Teradata/codesage-large-v2"
model_id  = "codesage-large-v2"        # arbitrary, used as the BYOM model_id
onnx_file = "onnx/model-ffn_skip.onnx"

# 1. Download the ONNX + tokenizer for the chosen variant.
hf_hub_download(repo_id=repo_id, filename=onnx_file,       local_dir="./")
hf_hub_download(repo_id=repo_id, filename="tokenizer.json", local_dir="./")

# 2. Connect to Vantage.
tdml.create_context(
    host=input("host: "),
    username=input("user: "),
    password=getpass.getpass("password: "),
)

# 3. Load model + tokenizer into BYOM tables (one-time per model_id).
tdml.save_byom(model_id=model_id, model_file=onnx_file,
               table_name="embeddings_models")
tdml.save_byom(model_id=model_id, model_file="tokenizer.json",
               table_name="embeddings_tokenizers")

Then call mldb.ONNXEmbeddings against an input table whose txt column carries the strings to embed:

This model emits a 2048-dimensional embedding. Teradata's wide-table output projection is capped at 2048 columns, which blocks the FLOAT32(2048) + Accumulate('id') projection that smaller models use. Pick the SQL form that matches your Vantage version:

Option A -- VARBYTE (works on TD 17.20 and TD 20.0+)

The vector lands as raw header-less float32 bytes (2048 * 4 = 8192 bytes), which fits in a single VARBYTE column and dodges the 2048-column cap. Decode on the client side or wrap the call in a UDF that returns FLOAT[].

SELECT *
FROM mldb.ONNXEmbeddings(
    ON (SELECT id, txt FROM your_input_table) AS InputTable
    ON (SELECT model_id, model FROM embeddings_models
         WHERE model_id = 'codesage-large-v2') AS ModelTable DIMENSION
    ON (SELECT model_id, tokenizer FROM embeddings_tokenizers
         WHERE model_id = 'codesage-large-v2') AS TokenizerTable DIMENSION
    USING
        Accumulate('id')
        ModelOutputTensor('sentence_embedding')
        OutputFormat('VARBYTE(8192)')
        OverwriteCachedModel('*')
) AS t
ORDER BY id;

Option B -- VECTOR (TD 20.0+ only)

Vantage 20.0 introduced a native VECTOR datatype that holds the full embedding as a single typed column, with native vector-similarity operators available on it.

SELECT *
FROM mldb.ONNXEmbeddings(
    ON (SELECT id, txt FROM your_input_table) AS InputTable
    ON (SELECT model_id, model FROM embeddings_models
         WHERE model_id = 'codesage-large-v2') AS ModelTable DIMENSION
    ON (SELECT model_id, tokenizer FROM embeddings_tokenizers
         WHERE model_id = 'codesage-large-v2') AS TokenizerTable DIMENSION
    USING
        Accumulate('id')
        ModelOutputTensor('sentence_embedding')
        OutputFormat('VECTOR')
        OverwriteCachedModel('*')
) AS t
ORDER BY id;

Use VECTOR if your Vantage version supports it; otherwise fall back to VARBYTE. Both forms emit the same underlying float32 values.

Pooling rule mean is applied inside the converted ONNX graph -- the output tensor named above already contains the pooled, post-processed embedding vector.

Original model attribution

The original weights and training methodology belong to the CodeSage authors. Please cite their work, not this repository, in academic contexts. The canonical upstream model card is at codesage/codesage-large-v2; refer to it for benchmarks, training details, intended use, and citation information.

Reporting issues

For ONNX-conversion or BYOM-compatibility issues specific to this Teradata-converted artifact, please open a Discussion on this model's Hugging Face page. Questions about the underlying model quality, training, or intended use should go to the upstream maintainer's model card.

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