Instructions to use Altworld/Astrea-R8-Chat-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Altworld/Astrea-R8-Chat-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Altworld/Astrea-R8-Chat-9B-GGUF",
	filename="Astrea-R8-Chat-9B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Altworld/Astrea-R8-Chat-9B-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 Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

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 Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

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 Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Altworld/Astrea-R8-Chat-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Altworld/Astrea-R8-Chat-9B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Altworld/Astrea-R8-Chat-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

Ollama
How to use Altworld/Astrea-R8-Chat-9B-GGUF with Ollama:
```
ollama run hf.co/Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Altworld/Astrea-R8-Chat-9B-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 Altworld/Astrea-R8-Chat-9B-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 Altworld/Astrea-R8-Chat-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Altworld/Astrea-R8-Chat-9B-GGUF to start chatting

How to use Altworld/Astrea-R8-Chat-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

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": "Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Altworld/Astrea-R8-Chat-9B-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 Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

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 Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Altworld/Astrea-R8-Chat-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

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 "Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M" \
  --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"

Docker Model Runner
How to use Altworld/Astrea-R8-Chat-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M
```

Lemonade

How to use Altworld/Astrea-R8-Chat-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Altworld/Astrea-R8-Chat-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Astrea-R8-Chat-9B-GGUF-Q4_K_M

List all available models

lemonade list

Astrea R8 Chat 9B — GGUF

Official text-only GGUF builds of Altworld/Astrea-R8-Chat-9B, a compact conversational and creative-writing model built on Qwen3.5-9B.

Try Astrea · API · Documentation

Files

Quant	Size	Use case
Q5_K_M	6.02 GiB	Recommended balance of quality and size
Q4_K_M	5.24 GiB	Smaller and faster; passed the same chat-routing smoke tests
Q6_K	6.85 GiB	Higher fidelity
Q8_0	8.87 GiB	Highest-fidelity quant in this repository

These are single-file, text-only models. The inherited vision components are not included because Astrea's visual behavior was not part of the release evaluation.

Run with llama.cpp

Use a current build of llama.cpp; Qwen3.5 support is recent.

llama-cli \
  -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q5_K_M \
  -cnv \
  --reasoning off \
  --temp 0.8 \
  --min-p 0.025 \
  --repeat-penalty 1.08

For an OpenAI-compatible local server:

llama-server \
  -hf Altworld/Astrea-R8-Chat-9B-GGUF:Q5_K_M \
  --reasoning off \
  --temp 0.8 \
  --min-p 0.025 \
  --repeat-penalty 1.08

Use --temp 0.2 for factual chat. The model carries its official chat template inside the GGUF and does not require a system prompt. Reasoning is disabled in the examples because Astrea's release behavior is direct response without a visible thinking block.

Validation

Each file was loaded and generated from with llama.cpp. The checks covered:

bare greeting with no system prompt;
native Astrea/Altworld identity;
ordinary factual chat;
supportive conversation;
creative-writing routing from a plain request with no scene card;
multi-turn recall using the embedded chat template.

Q4_K_M, the most aggressive quant here, correctly answered Good afternoon. as normal chat and produced a coherent story from Write a short story about a dragon who collects spoons. Q5_K_M correctly recalled a name across turns.

The GGUFs declare the base architecture's 262,144-token context. Actual usable context depends on your hardware, llama.cpp build, and runtime settings.

Conversion details

Converted from the official merged BF16 release with llama.cpp commit 76f46ad. The source checkpoint contains no MTP tensors, so conversion used --no-mtp. Quantization used llama.cpp's Q4_K_M, Q5_K_M, Q6_K, and Q8_0 presets.

About Astrea

Astrea was trained on top of Qwen3.5-9B using multi-round LoRA supervised fine-tuning, alternating conversational and narrative rounds and checking each round against Altworldbench. See the BF16 model card for benchmarks, methodology, examples, limitations, and the evaluation protocol.

Astrea is English-first and can still hallucinate. Verify high-stakes claims independently. Engine and quantization differences can affect output, so test your own prompts and settings.

License

Apache-2.0. See LICENSE and NOTICE.

Downloads last month: -

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for Altworld/Astrea-R8-Chat-9B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

Altworld/Astrea-R8-Chat-9B

Quantized

(4)

this model