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

Libraries

How to use silx-ai/Quasar-Preview with Transformers:

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

pipe = pipeline("text-generation", model="silx-ai/Quasar-Preview", 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-Preview", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use silx-ai/Quasar-Preview with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/silx-ai/Quasar-Preview

SGLang

How to use silx-ai/Quasar-Preview 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-Preview" \
    --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-Preview",
		"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-Preview" \
        --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-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

eyad-silx commited on Jun 19

Commit

ccabd08

verified ·

1 Parent(s): 489e7bd

Update modeling_quasar_long.py

Browse files

Files changed (1) hide show

modeling_quasar_long.py +1 -1

modeling_quasar_long.py CHANGED Viewed

@@ -391,7 +391,7 @@ class QuasarLongRotaryEmbedding(nn.Module):
         cos = cos.to(dtype=x.dtype)
         sin = sin.to(dtype=x.dtype)
         if _quasar_long_safe_nope_enabled(self.config):
-            cutoff = int(getattr(self.config, "nope_after_position", 512))
             nope_mask = (position_ids >= cutoff).unsqueeze(-1)
             if bool(nope_mask.any()):
                 cos = torch.where(nope_mask, torch.ones_like(cos), cos)

         cos = cos.to(dtype=x.dtype)
         sin = sin.to(dtype=x.dtype)
         if _quasar_long_safe_nope_enabled(self.config):
+            cutoff = int(getattr(self.config, "nope_after_position", 100485))
             nope_mask = (position_ids >= cutoff).unsqueeze(-1)
             if bool(nope_mask.any()):
                 cos = torch.where(nope_mask, torch.ones_like(cos), cos)