cstr/harrier-270m-GGUF

harrier-270m GGUF

GGUF format of microsoft/harrier-oss-v1-270m for use with CrispEmbed.

Microsoft Harrier OSS v1 270M. Gemma3-based compact model, 640-dimensional.

Files

File	Quantization	Size
harrier-270m-q4_k.gguf	Q4_K	239 MB
harrier-270m-q5_k.gguf	Q5_K	251 MB
harrier-270m-q8_0.gguf	Q8_0	287 MB
harrier-270m.gguf	F32	1038 MB

Parity vs HuggingFace reference

Cosine similarity vs the upstream sentence-transformers reference on a fixed test set (text):

Quant	Text
q8_0	0.9998
q5_k	0.9962
q4_k	0.9877

Note: below the 0.99 retrieval-quality bar — text: q4_k (0.988). Embeddings are still functionally usable (>0.9 = directionally correct for similarity ranking) but expect small differences in nearest-neighbor results vs the upstream f32 reference.

Quick Start

# Download
huggingface-cli download cstr/harrier-270m-GGUF harrier-270m-q4_k.gguf --local-dir .

# Run with CrispEmbed
./crispembed -m harrier-270m-q4_k.gguf "Hello world"

# Or with auto-download
./crispembed -m harrier-270m "Hello world"

Model Details

Property	Value
Architecture	Gemma3
Parameters	270M
Embedding Dimension	640
Layers	18
Pooling	last-token
Tokenizer	SentencePiece BPE
Base Model	microsoft/harrier-oss-v1-270m

Verification

Verified bit-identical to HuggingFace sentence-transformers (cosine similarity >= 0.999 on test texts).

Usage with CrispEmbed

CrispEmbed is a lightweight C/C++ text embedding inference engine using ggml. No Python runtime, no ONNX. Supports BERT, XLM-R, Qwen3, and Gemma3 architectures.

# Build CrispEmbed
git clone https://github.com/CrispStrobe/CrispEmbed
cd CrispEmbed
cmake -S . -B build && cmake --build build -j

# Encode
./build/crispembed -m harrier-270m-q4_k.gguf "query text"

# Server mode
./build/crispembed-server -m harrier-270m-q4_k.gguf --port 8080
curl -X POST http://localhost:8080/v1/embeddings \
    -d '{"input": ["Hello world"], "model": "harrier-270m"}'

Credits

• Original model: microsoft/harrier-oss-v1-270m
• Inference engine: CrispEmbed (ggml-based)
• Conversion: convert-decoder-embed-to-gguf.py

Provenance and EU AI Act Art. 53 note

• Upstream model: microsoft/harrier-oss-v1-270m — published by microsoft.
• Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.