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

Libraries

How to use silx-ai/Quasar-10B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="silx-ai/Quasar-10B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("silx-ai/Quasar-10B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use silx-ai/Quasar-10B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "silx-ai/Quasar-10B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silx-ai/Quasar-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/silx-ai/Quasar-10B

SGLang

How to use silx-ai/Quasar-10B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "silx-ai/Quasar-10B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silx-ai/Quasar-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "silx-ai/Quasar-10B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "silx-ai/Quasar-10B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

eyad-silx commited on Mar 9

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de77413

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Update README.md

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@@ -2,7 +2,7 @@
 language:
 - en
 - ar
-license: mit
 tags:
 - silx-ai
 - quasar
@@ -83,4 +83,4 @@ The Quasar roadmap continues toward even larger scales and deeper MoE integratio
 ---
 > [!IMPORTANT]
-> **Strategic Purpose**: Quasar-10B is designed as a foundational high-context engine. It will be used exclusively to **distill knowledge and generate synthetic reasoning data** for the upcoming **Quasar 22B MoE**, ensuring that the larger mixture-of-experts model inherits superior long-context coherence and refined logical state-trajectories from this fully linear base.

 language:
 - en
 - ar
+license: apache-2.0
 tags:
 - silx-ai
 - quasar
 ---
 > [!IMPORTANT]
+> **Strategic Purpose**: Quasar-10B is designed as a foundational high-context engine. It will be used exclusively to **distill knowledge and generate synthetic reasoning data** for the upcoming **Quasar 22B MoE**, ensuring that the larger mixture-of-experts model inherits superior long-context coherence and refined logical state-trajectories from this fully linear base.