SmolLM3
Collection
12 items • Updated • 7
How to use mlx-community/SmolLM3-3B-Base-bf16 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("mlx-community/SmolLM3-3B-Base-bf16")
prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)How to use mlx-community/SmolLM3-3B-Base-bf16 with Transformers.js:
// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';
// Allocate pipeline
const pipe = await pipeline('text-generation', 'mlx-community/SmolLM3-3B-Base-bf16');How to use mlx-community/SmolLM3-3B-Base-bf16 with MLX LM:
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/SmolLM3-3B-Base-bf16" --prompt "Once upon a time"
This model mlx-community/SmolLM3-3B-Base-bf16 was converted to MLX format from HuggingFaceTB/SmolLM3-3B-Base using mlx-lm version 0.25.3.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/SmolLM3-3B-Base-bf16")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
Quantized
Base model
HuggingFaceTB/SmolLM3-3B-Base