Instructions to use Michionlion/Astrea-R8-Chat-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Michionlion/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="Michionlion/Astrea-R8-Chat-9B-GGUF", filename="Astrea-R8-Chat-9B-Q8_0.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 Michionlion/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 Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf Michionlion/Astrea-R8-Chat-9B-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 Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Michionlion/Astrea-R8-Chat-9B-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 Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
Use Docker
docker model run hf.co/Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use Michionlion/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 "Michionlion/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": "Michionlion/Astrea-R8-Chat-9B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
- Ollama
How to use Michionlion/Astrea-R8-Chat-9B-GGUF with Ollama:
ollama run hf.co/Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
- Unsloth Studio
How to use Michionlion/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 Michionlion/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 Michionlion/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 Michionlion/Astrea-R8-Chat-9B-GGUF to start chatting
- Pi
How to use Michionlion/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 Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
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": "Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Michionlion/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 Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
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 Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Michionlion/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 Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
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 "Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0" \ --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 Michionlion/Astrea-R8-Chat-9B-GGUF with Docker Model Runner:
docker model run hf.co/Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
- Lemonade
How to use Michionlion/Astrea-R8-Chat-9B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Michionlion/Astrea-R8-Chat-9B-GGUF:Q8_0
Run and chat with the model
lemonade run user.Astrea-R8-Chat-9B-GGUF-Q8_0
List all available models
lemonade list
Astrea R8 Chat 9B — Q8_0 GGUF
This is an unofficial community Q8_0 GGUF conversion of Altworld/Astrea-R8-Chat-9B for llama.cpp, with an explicit non-reasoning chat template.
File
| File | Quantization | Size |
|---|---|---|
Astrea-R8-Chat-9B-Q8_0.gguf |
Q8_0 | 9,527,501,280 bytes (8.87 GiB) |
Embedded non-reasoning template
The GGUF contains a hard non-reasoning Jinja template in
tokenizer.chat_template; no external template file is required.
Astrea's optional thinking mode was not reliable in local llama.cpp testing:
simple prompts could consume hundreds of tokens before emitting </think>, and often did not end reasoning at all, and simply responded as if reasoning was not enabled.
The bundled template therefore always places a closed, empty thinking block in
the prompt and does not expose an enable_thinking template variable. It also
omits hidden reasoning when replaying assistant messages into conversation
history. A standalone copy is included as chat_template.jinja for inspection.
llama.cpp
llama-server.exe `
--model Astrea-R8-Chat-9B-Q8_0.gguf `
--jinja `
--reasoning off `
--reasoning-format none `
--ctx-size 32768 `
--n-gpu-layers all `
--temp 0.8 `
--top-p 1.0 `
--top-k 0 `
--min-p 0.025 `
--repeat-penalty 1.08
The model metadata advertises a 262,144-token context window. Choose a context
size appropriate for your available VRAM/RAM. The command above starts at a
more conservative 32,768 tokens. I was able to easily run a much more ambitous setup with -ngl all --fit off -c 147456 -np 4 --kv-unified on a 16GB VRAM card (5070 Ti).
Conversion notes
- The original safetensors were converted to BF16 GGUF with llama.cpp's
convert_hf_to_gguf.pyusing--no-mtp. The downloaded checkpoint did not contain the extra MTP-layer tensors declared by its configuration. - BF16 was quantized with
llama-quantizeusingQ8_0. - llama.cpp's
gguf_new_metadata.pyembedded the hard non-reasoning template; this metadata-only copy did not requantize tensors.
The tensor-only SHA-256 reported by llama-gguf-hash was identical before and
after the metadata rewrite:
20d213a0c5ee663ef6d02ffcff8d0b28cbff18b559c67aca5250cd5e6a22d624
The final whole-file checksums are in SHA256SUMS.
Validation
The final GGUF was loaded directly by llama-server without
--chat-template-file. Its exposed template matched the bundled standalone
Jinja, and a request that explicitly supplied enable_thinking=true still
returned normal content with no reasoning_content.
License and attribution
The source model is released under Apache-2.0. See LICENSE and NOTICE, and
refer to the source model card
for its intended use, evaluation results, and limitations.
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