Instructions to use Sao10K/Llama-3.3-70B-Vulpecula-r1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sao10K/Llama-3.3-70B-Vulpecula-r1 with Transformers:

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

pipe = pipeline("text-generation", model="Sao10K/Llama-3.3-70B-Vulpecula-r1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sao10K/Llama-3.3-70B-Vulpecula-r1")
model = AutoModelForCausalLM.from_pretrained("Sao10K/Llama-3.3-70B-Vulpecula-r1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sao10K/Llama-3.3-70B-Vulpecula-r1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sao10K/Llama-3.3-70B-Vulpecula-r1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sao10K/Llama-3.3-70B-Vulpecula-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sao10K/Llama-3.3-70B-Vulpecula-r1

SGLang

How to use Sao10K/Llama-3.3-70B-Vulpecula-r1 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 "Sao10K/Llama-3.3-70B-Vulpecula-r1" \
    --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": "Sao10K/Llama-3.3-70B-Vulpecula-r1",
		"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 "Sao10K/Llama-3.3-70B-Vulpecula-r1" \
        --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": "Sao10K/Llama-3.3-70B-Vulpecula-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sao10K/Llama-3.3-70B-Vulpecula-r1 with Docker Model Runner:
```
docker model run hf.co/Sao10K/Llama-3.3-70B-Vulpecula-r1
```

Update README.md

by gradientputri - opened Mar 20, 2025

base: refs/heads/main

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from: refs/pr/2

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README.md CHANGED Viewed

@@ -37,7 +37,7 @@ library_name: transformers
     <h2 style="color: #d35d6e; border-bottom: 2px solid #ff9eb5; padding-bottom: 10px;">🚀 Model Details</h2>
     <ul style="list-style: none; padding-left: 20px;">
       <li style="margin: 10px 0;">🌟 A thinking-based model inspired by Deepseek-R1, trained through both SFT and a little bit of RL on creative writing data.</li>
-      <li style="margin: 10px 0;">🧠 Prefill, or begin assistant replies with &lt;think&gt;\n to activate thinking mode.</li>
       <li style="margin: 10px 0;">🚀 Improved Steerability, instruct-roleplay and creative control over base model.</li>
     </ul>
   </div>

     <h2 style="color: #d35d6e; border-bottom: 2px solid #ff9eb5; padding-bottom: 10px;">🚀 Model Details</h2>
     <ul style="list-style: none; padding-left: 20px;">
       <li style="margin: 10px 0;">🌟 A thinking-based model inspired by Deepseek-R1, trained through both SFT and a little bit of RL on creative writing data.</li>
+      <li style="margin: 10px 0;">🧠 Prefill, or begin assistant replies with &lt;think&gt;\n to activate thinking mode, or not. It works well without thinking too.</li>
       <li style="margin: 10px 0;">🚀 Improved Steerability, instruct-roleplay and creative control over base model.</li>
     </ul>
   </div>