Instructions to use cstr/harrier-270m-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/harrier-270m-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cstr/harrier-270m-GGUF",
	filename="harrier-270m-q4_k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cstr/harrier-270m-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/harrier-270m-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf cstr/harrier-270m-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cstr/harrier-270m-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf cstr/harrier-270m-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf cstr/harrier-270m-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf cstr/harrier-270m-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf cstr/harrier-270m-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/harrier-270m-GGUF:Q8_0

Use Docker

docker model run hf.co/cstr/harrier-270m-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use cstr/harrier-270m-GGUF with Ollama:
```
ollama run hf.co/cstr/harrier-270m-GGUF:Q8_0
```

Unsloth Studio

How to use cstr/harrier-270m-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cstr/harrier-270m-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cstr/harrier-270m-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cstr/harrier-270m-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use cstr/harrier-270m-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/harrier-270m-GGUF:Q8_0
```

Lemonade

How to use cstr/harrier-270m-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/harrier-270m-GGUF:Q8_0

Run and chat with the model

lemonade run user.harrier-270m-GGUF-Q8_0

List all available models

lemonade list

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

Downloads last month: 129

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

8-bit

View +1 variant

Model tree for cstr/harrier-270m-GGUF

Base model

microsoft/harrier-oss-v1-270m

Quantized

(11)

this model