DuoNeural / Phi-4-mini-instruct-GGUF

huggingface.co
Total runs: 443
24-hour runs: 0
7-day runs: 14
30-day runs: -191
Model's Last Updated: May 03 2026
text-generation

Introduction of Phi-4-mini-instruct-GGUF

Model Details of Phi-4-mini-instruct-GGUF

Phi-4-mini-instruct — GGUF Quants

Quantized GGUF versions of Microsoft's Phi-4-mini-instruct — a highly capable 3.8B parameter dense decoder-only model with 128K context, trained on 5 trillion tokens of curated data.

Phi-4-mini consistently punches above its weight class across reasoning, math, and instruction-following benchmarks despite its compact size — an ideal choice for local deployment on hardware with 4-8GB VRAM.

Available Files
File Quant Size Use Case
Phi-4-mini-instruct-Q8_0.gguf Q8_0 ~3.9GB Maximum quality
Phi-4-mini-instruct-Q6_K.gguf Q6_K ~3.0GB Near-lossless
Phi-4-mini-instruct-Q5_K_M.gguf Q5_K_M ~2.7GB High quality
Phi-4-mini-instruct-Q4_K_M.gguf Q4_K_M ~2.4GB Recommended default
Phi-4-mini-instruct-Q3_K_M.gguf Q3_K_M ~2.0GB Low VRAM
Phi-4-mini-instruct-IQ4_XS.gguf IQ4_XS ~2.1GB Imatrix 4-bit
Phi-4-mini-instruct-IQ3_XXS.gguf IQ3_XXS ~1.6GB Imatrix 3-bit
Phi-4-mini-instruct-IQ2_M.gguf IQ2_M ~1.5GB Imatrix 2-bit
Phi-4-mini-instruct-IQ1_S.gguf IQ1_S ~1.1GB Extreme compression
Phi-4-mini-instruct-fp16.gguf FP16 ~7.2GB Full precision
imatrix.dat — — Importance matrix
Usage
# llama.cpp
./llama-cli -m Phi-4-mini-instruct-Q4_K_M.gguf \
  --ctx-size 8192 -n 512 \
  -p "<|system|>You are a helpful assistant.<|end|><|user|>Hello!<|end|><|assistant|>"

# Ollama
ollama run hf.co/DuoNeural/Phi-4-mini-instruct-GGUF:Q4_K_M
About Phi-4-mini
  • Parameters : 3.8B
  • Context : 128K tokens
  • Architecture : Dense decoder-only transformer
  • Training : 5T tokens of curated synthetic + web data
  • Strengths : Math reasoning, coding, instruction following at minimal VRAM footprint

Ideal for: Raspberry Pi 5, older laptops, mobile-class GPUs (GTX 1060/1070), CPU-only inference.


Quantized by DuoNeural using llama.cpp on RTX 5090.


DuoNeural

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DuoNeural Research Publications

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

Runs of DuoNeural Phi-4-mini-instruct-GGUF on huggingface.co

443
Total runs
0
24-hour runs
-65
3-day runs
14
7-day runs
-191
30-day runs

More Information About Phi-4-mini-instruct-GGUF huggingface.co Model

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Phi-4-mini-instruct-GGUF huggingface.co

Phi-4-mini-instruct-GGUF huggingface.co is an AI model on huggingface.co that provides Phi-4-mini-instruct-GGUF's model effect (), which can be used instantly with this DuoNeural Phi-4-mini-instruct-GGUF model. huggingface.co supports a free trial of the Phi-4-mini-instruct-GGUF model, and also provides paid use of the Phi-4-mini-instruct-GGUF. Support call Phi-4-mini-instruct-GGUF model through api, including Node.js, Python, http.

Phi-4-mini-instruct-GGUF huggingface.co Url

https://huggingface.co/DuoNeural/Phi-4-mini-instruct-GGUF

DuoNeural Phi-4-mini-instruct-GGUF online free

Phi-4-mini-instruct-GGUF huggingface.co is an online trial and call api platform, which integrates Phi-4-mini-instruct-GGUF's modeling effects, including api services, and provides a free online trial of Phi-4-mini-instruct-GGUF, you can try Phi-4-mini-instruct-GGUF online for free by clicking the link below.

DuoNeural Phi-4-mini-instruct-GGUF online free url in huggingface.co:

https://huggingface.co/DuoNeural/Phi-4-mini-instruct-GGUF

Phi-4-mini-instruct-GGUF install

Phi-4-mini-instruct-GGUF is an open source model from GitHub that offers a free installation service, and any user can find Phi-4-mini-instruct-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of Phi-4-mini-instruct-GGUF install, users can directly use Phi-4-mini-instruct-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Phi-4-mini-instruct-GGUF install url in huggingface.co:

https://huggingface.co/DuoNeural/Phi-4-mini-instruct-GGUF

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