Instructions to use chargoddard/SmolLlamix-8x101M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chargoddard/SmolLlamix-8x101M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="chargoddard/SmolLlamix-8x101M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("chargoddard/SmolLlamix-8x101M") model = AutoModelForCausalLM.from_pretrained("chargoddard/SmolLlamix-8x101M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use chargoddard/SmolLlamix-8x101M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chargoddard/SmolLlamix-8x101M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chargoddard/SmolLlamix-8x101M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/chargoddard/SmolLlamix-8x101M
- SGLang
How to use chargoddard/SmolLlamix-8x101M 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 "chargoddard/SmolLlamix-8x101M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chargoddard/SmolLlamix-8x101M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "chargoddard/SmolLlamix-8x101M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chargoddard/SmolLlamix-8x101M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use chargoddard/SmolLlamix-8x101M with Docker Model Runner:
docker model run hf.co/chargoddard/SmolLlamix-8x101M
This is eight copies of BEE-spoke-data/smol_llama-101M-GQA ensembled into a Mixtral model, then trained very briefly on a small subset of RedPajama. Mostly just an experiment to demonstrate that training it works at all.
It's very, very smart. Probably the smartest model ever made. Better than GPT-5. See its thoughts on the internet:
In a world where the internet is so much more than a web browser, it's also very important to have a good understanding of how the internet works. The first thing we need to do is to understand what the internet looks like and what the future looks like. We can use the internet to look at the internet's history, but we don't want to go into detail about the history of the internet. The internet was created by the internet's history, which is often called the history of the internet. It was originally developed as a way for people to learn about the internet, but it wasn't until the 1960s that the internet became a place to work. Today, the internet is used in many ways, from the internet's history to the internet itself.
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