LocalClaw/LLM/DFM-Mimir
Open-weight local LLM

DFM-Mimir

Catalogue summary: DFM-Mimir is a Danish and English 1B-class HRM language model trained from scratch by Danish Foundation Models, with Apache 2.0 weights, permissible-data positioning and GGUF artifacts for lightweight local inference.

Repository editorial metadata; verify comparative claims in the linked upstream material.

4 GB catalogue minimum4 GB RAMQ8_0Reasoning
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Parameters
1.8B
Minimum RAM
4 GB
Model size
1.8 GB
Quantization
Q8_0

Can DFM-Mimir run locally?

DFM-Mimir has a catalogue minimum of 4 GB RAM with Q8_0. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Use DFM-Mimir as the catalogue search term in a compatible runtime, and confirm the available format on the upstream repository before download.

Model sourcePublic GGUF repository (verified 2026-09-07): noctrex/DFM-MimirUpstream releasePaper or model cardLicence text
chatreasoninglightmultilingualgeneral

Install path

01
Check RAM fitMinimum 4 GB RAM. Start with the Q8_0 quant.
02
Load the modelSearch DFM-Mimir in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Catalogue record

  • Family: dfm
  • Parameters: 1.8B
  • Recommended quantization: Q8_0
  • Catalogue minimum RAM: 4 GB
  • Catalogue model size: 1.8 GB
  • Tags: chat, reasoning, light, multilingual, general

Practical limits

  • Catalogue RAM is a minimum estimate, not a guarantee for every context length or runtime.
  • Speed and memory use vary by quantization, backend, context length and system headroom.
  • Verify architecture, licence and usage restrictions in the linked upstream material before deployment.

Catalogue tags

  • chat
  • reasoning
  • light
  • multilingual
  • general

Capability profile

Repository catalogue ratings used by LocalClaw's editorial rubric. They are not a standardized third-party benchmark.

speed
10
quality
6
coding
5
reasoning
6

Technical notes

Developer
Danish Foundation Models
License
Apache 2.0
Context window
4,096 tokens
Architecture
1B-class HRM-Text causal language model trained from scratch, with roughly 1.79B BF16 parameters and a 4K context window.

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Entry laptop fitMacBook Air 8GBMore headroomMac mini M4 16GBAll compatible picks8GB RAM guide

Related catalogue entries

Linked mechanically by family, shared tags and nearby RAM tier; this is not a quality ranking.

Granite 4.1 (3B) 3BIbnSina-1.5B 1.5BMiniCPM5 1B 1BMiniCPM5 2B 2BDANTE-Mosaic-3.5B 3.08BGranite 4.2 (3B) 3BK2-Horizon-0.9B 0.9BLFM2.5-2.6B 2.7B

Where to go next