Instructions to use ClarenceDan/quasar-sn24-a5152 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClarenceDan/quasar-sn24-a5152 with Transformers:

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

pipe = pipeline("text-generation", model="ClarenceDan/quasar-sn24-a5152", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ClarenceDan/quasar-sn24-a5152", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ClarenceDan/quasar-sn24-a5152 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClarenceDan/quasar-sn24-a5152"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClarenceDan/quasar-sn24-a5152",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ClarenceDan/quasar-sn24-a5152

SGLang

How to use ClarenceDan/quasar-sn24-a5152 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 "ClarenceDan/quasar-sn24-a5152" \
    --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": "ClarenceDan/quasar-sn24-a5152",
		"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 "ClarenceDan/quasar-sn24-a5152" \
        --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": "ClarenceDan/quasar-sn24-a5152",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ClarenceDan/quasar-sn24-a5152 with Docker Model Runner:
```
docker model run hf.co/ClarenceDan/quasar-sn24-a5152
```

quasar-sn24-a5152

SN24 Quasar candidate for A51/A5152 UID207. Built from local A5187 step1200; local n300 block 8352000 mean_kl 2.602353 vs UID169 king 4.383146. Local DQ probe hard_pass=True.

Downloads last month: 6

Safetensors

Model size

3B params

Tensor type

F32

BF16

Model tree for ClarenceDan/quasar-sn24-a5152

Base model

Tobias1726/shadowfox-perturb-v1

Finetuned

(2)

this model