Instructions to use codelion/malm-165m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use codelion/malm-165m 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("codelion/malm-165m") 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 codelion/malm-165m with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "codelion/malm-165m" --prompt "Once upon a time"
- Xet hash:
- fa0e7e248af18dc9a2a42de14baf1d3c8564b717e8fbab4b0c21263b5b249976
- Size of remote file:
- 661 MB
- SHA256:
- 4f7c9ba2754706b3e7c9c0b00b53226a6bafdcb2aaab5b47a46bdf5d21e4f14f
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