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
- 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"
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- Learns semantic similarity between queries and function signatures
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- Uses attention-based retrieval over a memory bank
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## Related Work
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Part of the [HashHop](https://github.com/codelion/hash-hop) project exploring long-context evaluation and memory-augmented architectures.
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- Learns semantic similarity between queries and function signatures
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- Uses attention-based retrieval over a memory bank
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## Citation
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```bibtex
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@article{sharma2026malm,
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title={Reverse Engineering a $500M Mystery: From HashHop to Memory-Augmented Language Models},
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author={Sharma, Asankhaya},
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year={2026},
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url={https://huggingface.co/blog/codelion/reverse-engineering-magic-hashhop}
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}
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```
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## Related Work
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Part of the [HashHop](https://github.com/codelion/hash-hop) project exploring long-context evaluation and memory-augmented architectures.
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