Instructions to use sdkv2/falcon-h1-1.5b-mlx-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sdkv2/falcon-h1-1.5b-mlx-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("sdkv2/falcon-h1-1.5b-mlx-bf16") 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 sdkv2/falcon-h1-1.5b-mlx-bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "sdkv2/falcon-h1-1.5b-mlx-bf16" --prompt "Once upon a time"
- Atomic Chat
Falcon-H1 1.5B MLX BF16
MLX port of tiiuae/Falcon-H1-1.5B-Base for Apple Silicon.
For 4-bit: sdkv2/falcon-h1-1.5b-mlx-4bit
Usage
from mlx_lm import load, generate
model, tokenizer = load("sdkv2/falcon-h1-1.5b-mlx-bf16")
response = generate(model, tokenizer, prompt="Hello", verbose=True)
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Model size
2B params
Tensor type
BF16
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Hardware compatibility
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