How to use from the
Use from the
MLX library
# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir EuroMoE-2.6B-A0.6B-Instruct-2512-MLX-4bit luiscalisto/EuroMoE-2.6B-A0.6B-Instruct-2512-MLX-4bit

EuroMoE-2.6B-A0.6B-Instruct-2512-MLX-4bit

MLX 4-bit quantisation of utter-project/EuroMoE-2.6B-A0.6B-Instruct-2512, converted for use on Apple Silicon via mlx-lm.

Source model

  • Repository: utter-project/EuroMoE-2.6B-A0.6B-Instruct-2512
  • Release: 2025-12
  • Family: eurollm
  • Origin: eu
  • Languages / coverage: 35 languages, same EuroLLM EU coverage. Sparse MoE: 2.6B total params, 0.6B active per token.
  • License: apache-2.0 (inherited)

Notes from upstream

Mixture-of-experts variant from the EuroLLM team. Same EuroBlocks instruction tuning. config.json declares model_type=mixtral with 64 experts / 8 active per token, so mlx-lm's mixtral.py handles it.

Conversion details

  • Tool: mlx-lm 0.31.3
  • Quantisation: 4-bit (defaults from mlx_lm.convert)
  • Converted on: 2026-05-05

Usage

from mlx_lm import load, generate

model, tokenizer = load("luiscalisto/EuroMoE-2.6B-A0.6B-Instruct-2512-MLX-4bit")
prompt = "Hello, who are you?"
print(generate(model, tokenizer, prompt=prompt, max_tokens=128, verbose=False))

License and attribution

This is a quantised redistribution of utter-project/EuroMoE-2.6B-A0.6B-Instruct-2512. The original model and its license terms (apache-2.0) carry through unchanged. Please cite the upstream authors when using this model. See the source repository for the authoritative model card and citation.

Conversion provenance

Produced by llm-mlx-conversions, a small utility for publishing community MLX 4-bit quants of open-weight LLMs.

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