Microsoft’s Harrier: The Untapped Multilingual Model

Microsoft has just released something very interesting. They called it Harrier, named after a bird of prey known for hovering and precision, fitting for a model built around precise semantic understanding. It is a family of three multilingual text embedding models.

The key engineering decision here is that they used a decoder-only architecture, the same architecture family as GPT and LLaMA, rather than traditional encoder-only BERT-style models that have dominated embeddings for years. Not only will I install it and test it out, I am also going to unpack the differences between these three models. I will also share where each one fits best in production.

What Microsoft’s Harrier: The Untapped Multilingual Model does

Microsoft has shown results on MTEB, the Massive Text Embedding Benchmark. This model has performed very well and Microsoft is claiming state-of-the-art on that leaderboard. It is evaluated across dozens of tasks and languages.

If you are building multilingual applications, also see our take on cross-lingual chat systems in this multilingual guide.

To feed clean text into embeddings from scans or PDFs, pairing Harrier with a reliable OCR step helps. For that upstream stage, see our notes on choosing an OCR engine in this OCR model overview.

Install Microsoft’s Harrier: The Untapped Multilingual Model

Prerequisites for Microsoft’s Harrier: The Untapped Multilingual Model

conda create -n harrier python=3.10 -y
conda activate harrier

Install PyTorch with CUDA and core libraries.

pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install transformers accelerate sentencepiece huggingface_hub bitsandbytes

Verify that your GPU is visible.

python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_name(0))"

Download Microsoft’s Harrier: The Untapped Multilingual Model

python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='microsoft/harrier-oss-v1-27b', local_dir='harrier-oss-v1-27b', local_dir_use_symlinks=False)"

Encode text with Microsoft’s Harrier: The Untapped Multilingual Model

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

model_id = "microsoft/harrier-oss-v1-27b"

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
# For large models on 48 GB VRAM, prefer bfloat16 and device_map='auto'
model = AutoModel.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

def mean_pool(last_hidden_state, attention_mask):
    mask = attention_mask.unsqueeze(-1).type_as(last_hidden_state)
    masked = last_hidden_state * mask
    summed = masked.sum(dim=1)
    counts = mask.sum(dim=1).clamp(min=1e-9)
    return summed / counts

def encode_texts(texts):
    batch = tokenizer(
        texts,
        padding=True,
        truncation=True,
        max_length=32768,
        return_tensors="pt"
    )
    for k in batch:
        batch[k] = batch[k].to(model.device)
    with torch.no_grad():
        out = model(**batch)
        emb = mean_pool(out.last_hidden_state, batch["attention_mask"])
        emb = F.normalize(emb, p=2, dim=1)
    return emb.float().cpu()

queries = [
    "How much protein should a female eat?",
    "Define summit"
]

docs = [
    "As a general guideline, the average requirement of protein for women ages 19 to 70 is 46 g per day.",
    "A summit is the highest point of a hill or mountain."
]

q_emb = encode_texts(queries)
d_emb = encode_texts(docs)

![Screenshot from Microsoft’s Harrier: The Untapped Multilingual Model at 243s](/ai/microsoft-harrier/microsoft-harrier-243.webp)

# Dot product similarity matrix: shape [num_queries, num_docs]
scores = q_emb @ d_emb.T
print(scores)

If VRAM is tight, try 8-bit loading.

from transformers import AutoTokenizer, AutoModel
import torch

model = AutoModel.from_pretrained(
    model_id,
    load_in_8bit=True,
    device_map="auto"
)

Model sizes in Microsoft’s Harrier: The Untapped Multilingual Model

The 27 billion parameter model is in a completely different league. It outputs 5376 dimensional embeddings and reports an MTEB score around 74.3. All three share a generous 32k token context window.

When you plan for reliability in production, consider a fallback strategy so queries are never dropped. A simple approach is described in this fallback model pattern.

If you are balancing cost with a smaller general model in the stack, see the notes on a compact 7B class model here: Clara 7B model overview.

Multilingual support in Microsoft’s Harrier: The Untapped Multilingual Model

Coverage is heavily represented by European languages, with some Southeast Asian languages present. African languages felt sparse in the initial sweep, and it would be good to include more of them in future releases. Cross-lingual consistency remains the highlight.

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Resource notes for Microsoft’s Harrier: The Untapped Multilingual Model

Use cases for Microsoft’s Harrier: The Untapped Multilingual Model

Cross-lingual classification and tagging where label space stays stable but the input language varies. Reranking for retrieval pipelines when you need robust final ordering on tough queries. Similarity-based routing that picks the right tool or knowledge base by comparing query vectors to capability centroids.

Final thoughts on Microsoft’s Harrier: The Untapped Multilingual Model