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
File size: 370 Bytes
063093a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"model_type": "sol_lassi",
"architectures": ["SolLassi"],
"model_name": "Sol Lassi 600K Base",
"parameter_count": 600000,
"vocab_size": 2048,
"hidden_size": 96,
"num_hidden_layers": 6,
"num_attention_heads": 3,
"head_dim": 32,
"intermediate_size": 104,
"max_position_embeddings": 128,
"tie_word_embeddings": true,
"model_format": "mlx_npz"
}
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