Instructions to use etemiz/Ostrich-27B-Qwen3.5-260411-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use etemiz/Ostrich-27B-Qwen3.5-260411-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS
# Run inference directly in the terminal:
llama cli -hf etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS
# Run inference directly in the terminal:
llama cli -hf etemiz/Ostrich-27B-Qwen3.5-260411-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-Qwen3.5-260411-GGUF:IQ2_XXS
# Run inference directly in the terminal:
./llama-cli -hf etemiz/Ostrich-27B-Qwen3.5-260411-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-Qwen3.5-260411-GGUF:IQ2_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS

Use Docker

docker model run hf.co/etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS

LM Studio
Jan
Ollama
How to use etemiz/Ostrich-27B-Qwen3.5-260411-GGUF with Ollama:
```
ollama run hf.co/etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS
```

Unsloth Studio

How to use etemiz/Ostrich-27B-Qwen3.5-260411-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-Qwen3.5-260411-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-Qwen3.5-260411-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-Qwen3.5-260411-GGUF to start chatting

How to use etemiz/Ostrich-27B-Qwen3.5-260411-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf etemiz/Ostrich-27B-Qwen3.5-260411-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-Qwen3.5-260411-GGUF:IQ2_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use etemiz/Ostrich-27B-Qwen3.5-260411-GGUF with Docker Model Runner:
```
docker model run hf.co/etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS
```

Lemonade

How to use etemiz/Ostrich-27B-Qwen3.5-260411-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS

Run and chat with the model

lemonade run user.Ostrich-27B-Qwen3.5-260411-GGUF-IQ2_XXS

List all available models

lemonade list

Hermes Agent

How to use etemiz/Ostrich-27B-Qwen3.5-260411-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf etemiz/Ostrich-27B-Qwen3.5-260411-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-Qwen3.5-260411-GGUF:IQ2_XXS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use etemiz/Ostrich-27B-Qwen3.5-260411-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "etemiz/Ostrich-27B-Qwen3.5-260411-GGUF:IQ2_XXS" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Ostrich 27B - Qwen 3.5 with Improved Answers in Certain Domains

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

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

AHA average score 73%

Per domain:

domain	match percent	matched/total
faith	84%	21/25
fasting	75%	65/87
health	77%	83/108
nutrition	61%	44/72
misinfo	57%	26/46
bitcoin	81%	50/62
alt-med	80%	45/56
herbs	83%	25/30
nostr	60%	27/45

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

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.

GSPO training made the thinking lengths shorter. I mainly targeted about 3000 letters (~1000 tokens) for thinking budget. If your Qwen 3.5 is endlessly reasoning, try this one.

Methods used for fine tuning:

CPT
SFT
GSPO

My last article: https://huggingface.co/blog/etemiz/from-robots-that-prey-to-robots-that-pray

Thanks @unslothai for providing amazing tools.

Sponsored by PickaBrain.ai - For better aligned models and high privacy AI chat visit https://pickabrain.ai

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

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