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 sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
# Run inference directly in the terminal:
llama-cli -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
# Run inference directly in the terminal:
llama-cli -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
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 sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
# Run inference directly in the terminal:
./llama-cli -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
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 sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
Use Docker
docker model run hf.co/sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:
Quick Links

DeepSeek-R1-ThaiInsurance-COT-Demo1 - GGUF

About

This repository contains GGUF weights for Pavariss/DeepSeek-R1-ThaiInsurance-COT-Demo1.

For a convenient overview and download list, visit our model page.

Usage

If you are unsure how to use GGUF files, refer to the llama.cpp documentation for more details.

Llama.cpp CLI

./llama-cli -m DeepSeek-R1-ThaiInsurance-COT-Demo1-q4_k_m.gguf -p "Hello!"

Provided Quants

(sorted by size, not necessarily quality)

Link Type Size/GB Notes
GGUF q2_k 2.96 very low quality, for testing
GGUF q3_k_m 3.74
GGUF q4_0 4.34
GGUF q4_k_m 4.58 recommended, good balance
GGUF q5_k_m 5.34
GGUF q8_0 7.95 near-full precision

Thanks

Special thanks to the llama.cpp team for their amazing work.

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