Instructions to use etemiz/Ostrich-27B-Qwen3.5-260305-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use etemiz/Ostrich-27B-Qwen3.5-260305-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="etemiz/Ostrich-27B-Qwen3.5-260305-GGUF",
	filename="Ostrich-27B-Qwen3.5-260305-IQ2_XXS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use etemiz/Ostrich-27B-Qwen3.5-260305-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS
# Run inference directly in the terminal:
llama-cli -hf etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS
# Run inference directly in the terminal:
llama-cli -hf etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

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 etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS
# Run inference directly in the terminal:
./llama-cli -hf etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

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 etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

Use Docker

docker model run hf.co/etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

LM Studio
Jan
Ollama
How to use etemiz/Ostrich-27B-Qwen3.5-260305-GGUF with Ollama:
```
ollama run hf.co/etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS
```

Unsloth Studio

How to use etemiz/Ostrich-27B-Qwen3.5-260305-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 etemiz/Ostrich-27B-Qwen3.5-260305-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 etemiz/Ostrich-27B-Qwen3.5-260305-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for etemiz/Ostrich-27B-Qwen3.5-260305-GGUF to start chatting

How to use etemiz/Ostrich-27B-Qwen3.5-260305-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use etemiz/Ostrich-27B-Qwen3.5-260305-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

Run Hermes

hermes

Docker Model Runner
How to use etemiz/Ostrich-27B-Qwen3.5-260305-GGUF with Docker Model Runner:
```
docker model run hf.co/etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS
```

Lemonade

How to use etemiz/Ostrich-27B-Qwen3.5-260305-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull etemiz/Ostrich-27B-Qwen3.5-260305-GGUF:IQ2_XXS

Run and chat with the model

lemonade run user.Ostrich-27B-Qwen3.5-260305-GGUF-IQ2_XXS

List all available models

lemonade list

Ostrich 27B - Qwen 3.5 with Better Human Alignment

Ostrich LLMs, bringing you "the knowledge that matters".

Health, nutrition, medicinal herbs
Fasting, faith, healing
Liberating technologies like bitcoin and nostr

Methods used for fine tuning:

CPT
SFT
GSPO

Why: https://huggingface.co/blog/etemiz/building-a-beneficial-ai

GSPO training made the thinking lengths shorter. I mainly targeted about 3000 letters (~1000 tokens) for thinking budget.

Model is also abliterated since we built on @huihui-ai's model.

We plan to release many more based on Qwen 3.5 27B.

Comparison of some answers between another of our fine tune and base model: https://sheet.zohopublic.com/sheet/published/um332e3d15f34bfe64605ad3c1b149c9f8ca4 These answers are not from this model but it is a similar work.

Thanks @unslothai for providing amazing tools.

Sponsored by https://pickabrain.ai . A newer version of this runs there.

Downloads last month: 59

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

16-bit

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