Text Generation
MLX
English
sol_lassi
causal-lm
decoder-only
small-language-model
experimental
sol-intelligence
Instructions to use solintellegence/Sol-Lassi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use solintellegence/Sol-Lassi 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("solintellegence/Sol-Lassi") 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 solintellegence/Sol-Lassi with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "solintellegence/Sol-Lassi" --prompt "Once upon a time"
- Atomic Chat
Download tokenizer_config.json from solintellegence/Sol-Lassi: direct link, hf CLI and curl.
- Browser
- Download file 220 Bytes
-
https://huggingface.co/solintellegence/Sol-Lassi/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://solintellegence/Sol-Lassi/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/solintellegence/Sol-Lassi/resolve/main/tokenizer_config.json
220 Bytes
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|eos|>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "<|unk|>" | |
| } | |