Instructions to use AtlaAI/Selene-1-Mini-Llama-3.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AtlaAI/Selene-1-Mini-Llama-3.1-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AtlaAI/Selene-1-Mini-Llama-3.1-8B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AtlaAI/Selene-1-Mini-Llama-3.1-8B") model = AutoModelForCausalLM.from_pretrained("AtlaAI/Selene-1-Mini-Llama-3.1-8B", 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 AtlaAI/Selene-1-Mini-Llama-3.1-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AtlaAI/Selene-1-Mini-Llama-3.1-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AtlaAI/Selene-1-Mini-Llama-3.1-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B
- SGLang
How to use AtlaAI/Selene-1-Mini-Llama-3.1-8B 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 "AtlaAI/Selene-1-Mini-Llama-3.1-8B" \ --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": "AtlaAI/Selene-1-Mini-Llama-3.1-8B", "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 "AtlaAI/Selene-1-Mini-Llama-3.1-8B" \ --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": "AtlaAI/Selene-1-Mini-Llama-3.1-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use AtlaAI/Selene-1-Mini-Llama-3.1-8B with Docker Model Runner:
docker model run hf.co/AtlaAI/Selene-1-Mini-Llama-3.1-8B
This is a very good model
I've been using it mostly for general purpose question/answer, and some of the answers it provides show more knowledge accuracy and relevance than other small models like Mistral, Qwen, Llama, etc. Very surprised, since it seems to be built for a different purpose.
Are there any plans to launch larger versions? Would love to see a 14b, 24b, 32b.
It’s great to hear you’re loving the model! We’ve got a lot planned over the coming year regarding more powerful models and capabilities, you can sign up to get early access to our largest and most powerful model here:
Excellent, thanks. Looking forward!