SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 260
How to use thalyn-labs/SmolLM2-360M-4bit 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("thalyn-labs/SmolLM2-360M-4bit")
prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)How to use thalyn-labs/SmolLM2-360M-4bit with MLX LM:
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "thalyn-labs/SmolLM2-360M-4bit" --prompt "Once upon a time"
A 4-bit MLX conversion of HuggingFaceTB/SmolLM2-360M, the base model (no instruction tuning), published by Thalyn Labs for Kaleidokin, a game about what a language model is, learned by raising one. It is the game's second kind of newborn: the English-trained, smaller one. The weights are exactly the upstream weights, quantized; nothing was trained.
model_type: llama), 362M parameters, 49,152-token vocabulary, tied embeddings.mlx-lm on 2026-09-06:mlx_lm.convert --hf-path HuggingFaceTB/SmolLM2-360M -q --q-bits 4 --q-group-size 64 --mlx-path SmolLM2-360M-4bit
Loads with mlx-lm (Python) and mlx-swift-lm (Swift) like any mlx-community model:
mlx_lm.generate --model thalyn-labs/SmolLM2-360M-4bit --prompt "The light through the window was" --ignore-chat-template
Licence: Apache 2.0, as upstream. Cite SmolLM2 (arXiv:2502.02737) for the model itself.
4-bit
Base model
HuggingFaceTB/SmolLM2-360M