Instructions to use sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1",
	filename="DeepSeek-R1-ThaiInsurance-COT-Demo1-q2_k.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 with 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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:Q4_K_M

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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:Q4_K_M

Use Docker

docker model run hf.co/sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:Q4_K_M

LM Studio
Jan
Ollama
How to use sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 with Ollama:
```
ollama run hf.co/sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:Q4_K_M
```

Unsloth Studio new

How to use sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 to start chatting

Docker Model Runner
How to use sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 with Docker Model Runner:
```
docker model run hf.co/sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:Q4_K_M
```

Lemonade

How to use sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sthaps/DeepSeek-R1-ThaiInsurance-COT-Demo1:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-ThaiInsurance-COT-Demo1-Q4_K_M

List all available models

lemonade list

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.

Downloads last month: 97

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit

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