harrier-oss

About

What harrier-oss is

harrier-oss-v1 harrier-oss-v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder-only architectures with last-token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to retrieval, clustering, semantic similarity, classification, bitext mining, and reranking. The models achieve state-of-the-art results on the Multilingual MTEB v2 benchmark as of the release date. Training All models are trained with contrastive learning objectives on a large-scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Sentence Transformers Have a look at configsentencetransformers.json for the prompts that are pre-configured, such as websearchquery, stsquery, and bitextquery. You can also use a custom instruction directly via e.g. model.encode(queries, prompt="Instruct: Retrieve semantically similar text\nQuery: "). Transformers Supported Languages The models are trained on multilingual data and support a wide range of languages, including but not limited to: Arabic, Bulgarian, Catalan, Czech, Danish, German, Greek, English, Spanish, Estonian, Persian, Finnish, French, Hebrew, Hindi, Croatian, Hungarian, Indonesian, Italian, Japanese, Korean, Lithuanian, Latvian, Macedonian, Malay, Dutch, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Albanian, Serbian, Swedish, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Chinese. Evaluation Please follow the mteb repository on how to reproduce our scores. The evaluation prompts used for each task are also available at mtebv2evalprompts.json. FAQ 1. Do I need to add instructions to the query? Yes, this is how the model is trained, otherwise you will see a performance degradation. The task definition should be a one-sentence instruction that describes the task. This is a way to customize text embeddings for different scenarios through natural language instructions. On the other hand, t

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys harrier-oss for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (harrier-oss below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"harrier-oss","input":"text to embed"}'

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

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