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: 850 Bytes
063093a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"model_name": "Sol Lassi 600K Base",
"backend": "MLX",
"architecture": "DenseControlLM / dense-deep",
"parameters": 600000,
"optimizer": "M-SimOW",
"optimizer_beta": 0.8,
"weight_decay": 0.1,
"learning_rate_schedule": [
{"tokens": 500000000, "learning_rate": 0.006},
{"tokens": 500000000, "learning_rate": 0.003}
],
"training_tokens": 1000000000,
"context_length": 128,
"batch_size": 32,
"seed": 7,
"dataset": "HuggingFaceFW/finephrase",
"dataset_revision": "78cf4a5ed0099214979c094c963e699c19163838",
"dataset_manifest": "finephrase-balanced-500m-2k-v2",
"dataset_configs": ["faq", "math", "table", "tutorial"],
"dataset_split_method": "source-id-disjoint SHA-256 split",
"training_loss_note": "The trainer recorded per-minibatch loss; no validation or benchmark result is claimed in this release."
}
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