Instructions to use Sao10K/70B-L3.3-Cirrus-x1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sao10K/70B-L3.3-Cirrus-x1 with Transformers:

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

pipe = pipeline("text-generation", model="Sao10K/70B-L3.3-Cirrus-x1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sao10K/70B-L3.3-Cirrus-x1")
model = AutoModelForCausalLM.from_pretrained("Sao10K/70B-L3.3-Cirrus-x1")
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

vLLM

How to use Sao10K/70B-L3.3-Cirrus-x1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sao10K/70B-L3.3-Cirrus-x1"
# 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/70B-L3.3-Cirrus-x1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sao10K/70B-L3.3-Cirrus-x1

SGLang

How to use Sao10K/70B-L3.3-Cirrus-x1 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/70B-L3.3-Cirrus-x1" \
    --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/70B-L3.3-Cirrus-x1",
		"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/70B-L3.3-Cirrus-x1" \
        --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/70B-L3.3-Cirrus-x1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sao10K/70B-L3.3-Cirrus-x1 with Docker Model Runner:
```
docker model run hf.co/Sao10K/70B-L3.3-Cirrus-x1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

70B-L3.3-Cirrus-x1

- Same data composition as Freya, applied differently, trained longer too.
- Merging with its checkpoints was also involved.
- Has a nice style, with occasional issues that can be easily fixed.
- A more stable version compared to previous runs.

My Model Settings | Feel free to use DRY or XTC or whatever meme samplers. I have zero experience with them, I can't help you there.

Prompt Format: Llama-3-Instruct
Temperature: 1.1
min_p: 0.05

Training time in total was ~22 Hours on a 8xH100 Node.
Then, ~3 Hours spent merging multiple epoch checkpoints through dare_ties and model experimentation on a 2xH200 Node.

Compute spent is on my own budget and wallet, through Runpod or Vast.

Probably my last main release in a while unless things change, spent too much on this.

https://sao10k.carrd.co/ for contact. Ideally on Discord or here on HF.

If you're interested in donations:

My Metamask Wallet: 0xb2f71C762990e1FC3353319fA63f2C65249d9379

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