| --- |
| license: mit |
| base_model: |
| - intfloat/multilingual-e5-large-instruct |
| tags: [llmcompressor, compressed-tensors, quantized, fp8, vllm] |
| pipeline_tag: feature-extraction |
| library_name: vllm |
| --- |
| |
| # multilingual-e5-large-instruct-fp8 |
|
|
| `FP8_DYNAMIC` quantization of [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct), produced with |
| [llm-compressor](https://github.com/vllm-project/llm-compressor) 0.13.0 and measured end to end on |
| NVIDIA H100 80 GB with vLLM 0.26.0. |
|
|
| Only the Linear layers of the encoder are quantized; the vocabulary, position and token-type |
| embeddings, the pooler and any classification head stay in bf16. |
|
|
| ## Measured |
|
|
| | variant | size | SciFact nDCG@10 | STS-B de ρ | STS17 en-de ρ | texts/s (bs=256) | |
| |---|---|---|---|---|---| |
| | bf16 source | 1068 MiB | 0.7204 | 0.8347 | 0.8525 | 1488 | |
| | FP8 | 802 MiB | 0.7164 | 0.8349 | 0.8526 | 1511 | |
| | NVFP4 | 675 MiB | 0.7036 | 0.8371 | 0.8440 | – | |
|
|
| Retrieval and STS are unchanged against bf16. Size is **−25%**. Throughput is unchanged — the gain is memory only. If you want the smaller checkpoint, [the NVFP4 sibling](https://huggingface.co/mbehr90/multilingual-e5-large-instruct-nvfp4) is 675 MiB for 1.7 pp nDCG@10. |
|
|
| SciFact: 300 queries over 5183 documents, nDCG@10. STS-B de: mteb/stsb_multi_mt (1379 pairs). |
| STS17 en-de: mteb/sts17-crosslingual-sts (250 pairs). Reranking: 1 positive + 19 sampled negatives |
| per query. Throughput over 4096 multilingual texts through the vLLM OpenAI endpoint. |
|
|
|
|
| ## Serving |
|
|
| ```bash |
| vllm serve mbehr90/multilingual-e5-large-instruct-fp8 --runner pooling |
| ``` |
|
|
