| --- |
| license: mit |
| base_model: microsoft/codebert-base |
| base_model_relation: quantized |
| library_name: gguf |
| pipeline_tag: feature-extraction |
| tags: |
| - gguf |
| - embeddings |
| - code |
| - llama.cpp |
| --- |
| |
| # CodeBERT base — GGUF Q8_0 |
| |
| [`microsoft/codebert-base`](https://huggingface.co/microsoft/codebert-base) converted to GGUF at Q8_0 for |
| llama.cpp's `llama-server --embeddings`. |
|
|
| | | | |
| |---|---| |
| | File | `codebert-base-Q8_0.gguf` (135 MB) | |
| | SHA-256 | `01a85fb726bf4dc063aaac34b33fde08f991b96b3f9f76ff10e28fa2269df5ea` | |
| | Source revision | `99d7ef814601faaf7bdc2f774ffa7dade4f4d828` (safetensors) | |
| | Context | 512 tokens | |
| | Pooling | mean (stored in the file, no `--pooling` flag needed) | |
| | Dimensions | 768 | |
|
|
| ```sh |
| llama-server -m codebert-base-Q8_0.gguf --embeddings --port 8080 |
| curl -s localhost:8080/v1/embeddings -H 'Content-Type: application/json' -d '{"input":["def f(): pass"]}' |
| ``` |
|
|
| ## How it was converted |
|
|
| `convert_hf_to_gguf.py` (llama.cpp, 2026-10-01) with `--outtype q8_0`, after two additions to the source |
| directory: |
|
|
| - a `tokenizer.json`, written by `RobertaTokenizerFast.save_pretrained`. The upstream repo ships only |
| `vocab.json` + `merges.txt`, and without `tokenizer.json` the converter falls back to a WordPiece |
| vocabulary and tokenizes code wrongly; |
| - a sentence-transformers `modules.json` + `1_Pooling/config.json` declaring mean pooling, so the pooling |
| type is written into the GGUF. |
|
|
| ## Verification |
|
|
| 40 random code/console/output blocks (first 600 characters), embedded by this file on llama-server (CPU) and |
| by the original model in PyTorch fp32 with masked mean pooling over the last hidden state. Token ids are |
| identical; cosine similarity min 0.9998, median 0.99994. |
|
|
| The weights and their licence (MIT) are Microsoft's; see the [CodeBERT paper](https://arxiv.org/abs/2002.08155). |
|
|
