Instructions to use Akicou/Quasar-10B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Akicou/Quasar-10B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Akicou/Quasar-10B-GGUF",
	filename="Quasar-10B-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Akicou/Quasar-10B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Akicou/Quasar-10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Akicou/Quasar-10B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Akicou/Quasar-10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Akicou/Quasar-10B-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 Akicou/Quasar-10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Akicou/Quasar-10B-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 Akicou/Quasar-10B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Akicou/Quasar-10B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Akicou/Quasar-10B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Akicou/Quasar-10B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Akicou/Quasar-10B-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": "Akicou/Quasar-10B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Akicou/Quasar-10B-GGUF:Q4_K_M

Ollama
How to use Akicou/Quasar-10B-GGUF with Ollama:
```
ollama run hf.co/Akicou/Quasar-10B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Akicou/Quasar-10B-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 Akicou/Quasar-10B-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 Akicou/Quasar-10B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Akicou/Quasar-10B-GGUF to start chatting

Pi new

How to use Akicou/Quasar-10B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Akicou/Quasar-10B-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": "Akicou/Quasar-10B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Akicou/Quasar-10B-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 Akicou/Quasar-10B-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 Akicou/Quasar-10B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Akicou/Quasar-10B-GGUF with Docker Model Runner:
```
docker model run hf.co/Akicou/Quasar-10B-GGUF:Q4_K_M
```

Lemonade

How to use Akicou/Quasar-10B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Akicou/Quasar-10B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Quasar-10B-GGUF-Q4_K_M

List all available models

lemonade list

Quasar-10B-GGUF

GGUF conversion and quantizations of silx-ai/Quasar-10B.

Upstream metadata lists Qwen/Qwen3.5-9B-Base as the base model for silx-ai/Quasar-10B; this repository is a GGUF quantization/conversion of silx-ai/Quasar-10B.

These files were converted/quantized with my llama.cpp fork:

https://github.com/Akicou/llama.cpp

They can also be run with that fork. If upstream llama.cpp does not recognize the model architecture or metadata, use the fork above.

Available files

File	Type	Size
`Quasar-10B-f16.gguf`	F16 GGUF	17,217,163,520 bytes
`Quasar-10B-Q4_K_M.gguf`	Q4_K_M quantization	5,428,039,936 bytes
`Quasar-10B-Q4_K_S.gguf`	Q4_K_S quantization	5,153,411,328 bytes

Runtime

Example using the forked llama.cpp:

git clone https://github.com/Akicou/llama.cpp
cd llama.cpp
cmake -B build
cmake --build build --config Release -j

hf download Akicou/Quasar-10B-GGUF Quasar-10B-Q4_K_M.gguf --local-dir .
./build/bin/llama-cli -m Quasar-10B-Q4_K_M.gguf -p "hi"

Adjust the binary path for your platform/build type if needed.

Important behavior note

While testing, this model did not seem to be fine-tuned for instruction following or normal conversational/chat use. It appears to behave more like a continued-pretraining checkpoint. For example, even when prompted with something simple like hi, it tended to act as if it had been asked to solve a mathematical problem using constraints.

Because of that, do not expect reliable assistant-style instruction following from these files unless you add your own prompting/evaluation setup or fine-tune the model further.

Model card metadata

Base model: silx-ai/Quasar-10B
Base-model relation: quantized GGUF conversion
Upstream base listed by silx-ai/Quasar-10B: Qwen/Qwen3.5-9B-Base
License: Apache-2.0, inherited from the upstream model metadata
Languages listed upstream: English (en) and Arabic (ar)
Pipeline: text generation

GGUF metadata reported by Hugging Face Hub

The Hub detected the following GGUF metadata from the uploaded files:

Architecture: quasar
Context length: 2,097,152
EOS token: <|endoftext|>
Chat template: present in GGUF metadata
Total file size for the detected F16 GGUF: 17,217,163,520 bytes
Repo contains F16 plus Q4_K_M and Q4_K_S GGUF files

Downloads last month: 206

GGUF

Model size

9B params

Architecture

quasar

Hardware compatibility

4-bit

16-bit

Model tree for Akicou/Quasar-10B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

silx-ai/Quasar-10B

Quantized

(1)

this model