Instructions to use sugoitoolkit/Sugoi-14B-Ultra-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sugoitoolkit/Sugoi-14B-Ultra-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sugoitoolkit/Sugoi-14B-Ultra-GGUF",
	filename="Sugoi-14B-Ultra-F16.gguf",
)

llm.create_chat_completion(
	messages = "\"Меня зовут Вольфганг и я живу в Берлине\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sugoitoolkit/Sugoi-14B-Ultra-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf sugoitoolkit/Sugoi-14B-Ultra-GGUF: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 sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sugoitoolkit/Sugoi-14B-Ultra-GGUF: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 sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use sugoitoolkit/Sugoi-14B-Ultra-GGUF with Ollama:
```
ollama run hf.co/sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M
```

Unsloth Studio new

How to use sugoitoolkit/Sugoi-14B-Ultra-GGUF 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 sugoitoolkit/Sugoi-14B-Ultra-GGUF 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 sugoitoolkit/Sugoi-14B-Ultra-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sugoitoolkit/Sugoi-14B-Ultra-GGUF to start chatting

Pi new

How to use sugoitoolkit/Sugoi-14B-Ultra-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sugoitoolkit/Sugoi-14B-Ultra-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use sugoitoolkit/Sugoi-14B-Ultra-GGUF with Docker Model Runner:
```
docker model run hf.co/sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M
```

Lemonade

How to use sugoitoolkit/Sugoi-14B-Ultra-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sugoitoolkit/Sugoi-14B-Ultra-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Sugoi-14B-Ultra-GGUF-Q4_K_M

List all available models

lemonade list

Sugoi LLM 14B Ultra (GGUF version)

Unleashing the full potential of the previous sugoi 14B model, Sugoi 14B Ultra delivers near-double translation accuracy compared to its quantized predecessor—achieving a BLEU score of 21.38 vs 13.67. Its prompt-following skills rival those of Qwen 2.5 Base, especially when handling the bracket-heavy text commonly found in RPG Maker projects.

Model Overview

Key Improvements
- Nearly 2× BLEU score boost over previous quantized version (21.38 vs 13.67).
- Stronger prompt adherence, especially with RPGM-style bracketed text.
Ideal Use Cases
- Japanese → English translation—especially for game dialogue or RPG text.
- Interactive environments—works well with chat UIs like LM Studio.

System Prompt & Settings

Must include a system prompt for best performance:

You are a professional localizer whose primary goal is to translate Japanese to English. You should use colloquial or slang or nsfw vocabulary if it makes the translation more accurate. Always respond in English.

Additional recommendations:

Context length: ~10 lines (too much may degrade quality).
In LM Studio, you can interactively ask grammar or context questions, or switch target language via the prompt (quality may vary).

Experimental Features

These features are experimental and may need tuning:

Tool Integration & JSON Output
RPGM Tag Preservation

Recommended Sampling Parameters

Parameter	Value
Temperature	0.1
Top-K	40
Top-P	0.95
Min-P	0.05
Repeat Penalty	1.1

Evaluation & Comparison

Quantitative: BLEU score doubled vs prior version (21.38 vs 13.67).
Qualitative: Effective with prompt complexity and RPG Maker markup—delivers clean and accurate translations.

Limitations & Usage Notes

Overly long context may “poison” the output—keep it around 10 lines for best results.
Experimental features like JSON formatting and tag preservation may not always work perfectly—review outputs carefully.
Performance may vary depending on the prompt complexity and UI/tool environment.
Only uncensored for translation task with translation system prompt, other use case such as roleplay,chat may still trigger qwen censoring.