DFM Mimir — GGUF

Official GGUF conversions of DFM Mimir from Danish Foundation Models, prepared for local inference using the DFM Mimir app and patched llama.cpp. See the original model card for training, evaluation, intended uses and limitations.

Choose a file

Download one GGUF; each is self-contained with its tokenizer and chat template. Q4_K_M is the smallest option. Q8_0 and BF16 require more memory. Download sizes are decimal GB and do not include the additional RAM required for inference. BF16 is an unquantized conversion; both quantized variants were made directly from this BF16 export, not from another quantized file.

For example:

hf download danish-foundation-models/DFM-Mimir-GGUF dfm-mimir-q4_k_m.gguf --local-dir ./mimir

Runtime and chat template

This architecture uses PrefixLM. Use the DFM Mimir app or the schneiderkamplab llama.cpp fork with HRM-Text/PrefixLM support; these files are not a claim of compatibility with arbitrary stock llama.cpp, Ollama or other GGUF applications. Always use the embedded Mimir chat template. Raw prompts or a substitute chat template do not reproduce the model's training input format.

The model was trained with a context length of 4096 tokens. Larger contexts are experimental and require additional memory and validation.

Training-faithful tokenizer correction

These exports load the original tokenizer.json directly, matching the training pipeline. They use tokenizer.ggml.pre=gemma4 and preserve all 256 byte-fallback token types. An earlier conversion path applied the exported HF configuration's fix_mistral_regex=true flag and selected a different pre-tokenizer; that behavior did not match training. These files correct that conversion without changing the BF16 model tensors.

Provenance and validation

• Source: danish-foundation-models/DFM-Mimir, revision 2844f0178e695d7d9ce182cb660671fd34c76ce5.
• Converter: schneiderkamplab/llama.cpp, revision 4122b9a81.
• Each precision passes 724/724 tokenizer, decoder and chat-template checks, and 19/19 independent training-tokenizer audit cases.
• Each precision passes short Danish and English generation checks on both macOS CPU and Metal, using Mimir's chat template.
• All 259 BF16 tensor payloads are bit-identical to the previous BF16 conversion; the correction affects tokenizer metadata.

These checks are bounded artifact validation, not new full benchmark scores or qualification of every accelerator, mobile device or extended context length.

SHA256SUMS records exact file checksums. provenance.json records source and conversion metadata. validation.json records the actual local validation results. See the reproduction report for commands and scope. License: Apache 2.0, inherited from the source model.