Text Generation
MLX
Safetensors
PyTorch
English
code-autocomplete
discrete-diffusion
masked-diffusion
autoregressive
Instructions to use Kazenowoko/telos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Kazenowoko/telos with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Kazenowoko/telos") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use Kazenowoko/telos with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "Kazenowoko/telos" --prompt "Once upon a time"
- Atomic Chat
Download data/python_corpus_5b.json from Kazenowoko/telos: direct link, hf CLI and curl.
- Browser
- Download file 40 Bytes
-
https://huggingface.co/Kazenowoko/telos/resolve/main/data/python_corpus_5b.json
- Command line
-
hf download hf://Kazenowoko/telos/data/python_corpus_5b.json
-
curl -L -o python_corpus_5b.json https://huggingface.co/Kazenowoko/telos/resolve/main/data/python_corpus_5b.json
40 Bytes
| {"dtype": "uint16", "vocab_size": 8192} | |