How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
# Run inference directly in the terminal:
llama-cli -hf roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
# Run inference directly in the terminal:
llama-cli -hf roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
# Run inference directly in the terminal:
./llama-cli -hf roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
# Run inference directly in the terminal:
./build/bin/llama-cli -hf roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
Use Docker
docker model run hf.co/roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF:Q3_K_L
Quick Links

roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF

Repo: roleplaiapp/DS-R1-Distill-Q2.5-14B-Harmony_V0.1-Q3_K_L-GGUF Original Model: DS-R1-Distill-Q2.5-14B-Harmony_V0.1 Quantized File: DS-R1-Distill-Q2.5-14B-Harmony_V0.1.Q3_K_L.gguf Quantization: GGUF Quantization Method: Q3_K_L

Overview

This is a GGUF Q3_K_L quantized version of DS-R1-Distill-Q2.5-14B-Harmony_V0.1

Quantization By

I often have idle GPUs while building/testing for the RP app, so I put them to use quantizing models. I hope the community finds these quantizations useful.

Andrew Webby @ RolePlai.

Downloads last month
5
GGUF
Model size
15B params
Architecture
qwen2
Hardware compatibility
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3-bit

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