Open-weight local LLM

NuExtract (3.8B)

Catalogue summary: Information extraction specialist on Phi-3. Great for structured data extraction. 43K downloads.

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

6 GB catalogue minimum6 GB RAMQ4_K_MFast chat
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Parameters
3.8B
Minimum RAM
6 GB
Model size
2.4 GB
Quantization
Q4_K_M

Can NuExtract (3.8B) run locally?

NuExtract (3.8B) has a catalogue minimum of 6 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

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

lightspeed

Install path

01
Check RAM fitMinimum 6 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch nuextract in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Catalogue record

  • Family: nuextract
  • Parameters: 3.8B
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 6 GB
  • Catalogue model size: 2.4 GB
  • Tags: light, speed

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

  • light
  • speed

Capability profile

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

speed
9
quality
5
coding
3
reasoning
5

Technical notes

Developer
See upstream repository
License
See upstream repository
Context window
See upstream repository
Architecture
See upstream repository

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.

Related catalogue entries

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

Where to go next