cstr/harrier-0.6b-GGUF

harrier-0.6b GGUF

GGUF format of microsoft/harrier-oss-v1-0.6b for use with CrispEmbed and Ollama.

Files

Recommended: Q80 for quality (cos vs HF: L2=1.0), Q4K for size (L2=1.0).

Quick Start

CrispEmbed

./crispembed -m harrier-0.6b "Hello world"
./crispembed-server -m harrier-0.6b --port 8080

Ollama (with CrispStrobe fork)

echo "FROM harrier-0.6b-q8_0.gguf" > Modelfile
ollama create harrier-0.6b -f Modelfile
curl http://localhost:11434/api/embed -d '{"model":"harrier-0.6b","input":["Hello world"]}'

Python (CrispEmbed)

from crispembed import CrispEmbed
model = CrispEmbed("harrier-0.6b-q8_0.gguf")
vectors = model.encode(["Hello world", "Goodbye world"])

Model Details

Server API

CrispEmbed server supports four API dialects:

• —POST /embed -- native
• —POST /v1/embeddings -- OpenAI-compatible
• —POST /api/embed -- Ollama-compatible
• —POST /api/embeddings -- Ollama legacy

Credits

• —Original model: microsoft/harrier-oss-v1-0.6b
• —Inference: CrispEmbed (MIT, ggml-based)

Provenance and EU AI Act Art. 53 note

• —Upstream model: microsoft/harrier-oss-v1-0.6b — 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.