Instructions to use Sao10K/L3.3-70B-Euryale-v2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sao10K/L3.3-70B-Euryale-v2.3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sao10K/L3.3-70B-Euryale-v2.3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Sao10K/L3.3-70B-Euryale-v2.3") model = AutoModelForCausalLM.from_pretrained("Sao10K/L3.3-70B-Euryale-v2.3", 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/L3.3-70B-Euryale-v2.3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sao10K/L3.3-70B-Euryale-v2.3" # 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/L3.3-70B-Euryale-v2.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Sao10K/L3.3-70B-Euryale-v2.3
- SGLang
How to use Sao10K/L3.3-70B-Euryale-v2.3 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/L3.3-70B-Euryale-v2.3" \ --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/L3.3-70B-Euryale-v2.3", "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/L3.3-70B-Euryale-v2.3" \ --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/L3.3-70B-Euryale-v2.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Sao10K/L3.3-70B-Euryale-v2.3 with Docker Model Runner:
docker model run hf.co/Sao10K/L3.3-70B-Euryale-v2.3
Context size on openrouter
Hey @Sao10K , I was trying to test your model on OpenRouter, but it seems to be artificially limited to 8K context size. Is that on purpose or just a mistake?
Link, just in case: https://openrouter.ai/sao10k/l3.3-euryale-70b
I have no clue how it works on OR, it depends on provider?
I don't control what they host
Eury v2.3 is a Llama 3.3 model so it's fine up to 128k as the base is, though I'd recommend up to 32k
I thought you were the one posting it (no idea how that shit works either, I just saw you name and assumed you had some level of control).
Welp, I'll ask them, but it's probably a bust as they likely do it to save processing power/time.
Thanks for the quick answer. (and yep, i assumed as much about the 32k-ish tks)
Cheers.
EDIT: As expected, they do it on purpose.