Instructions to use dikiyplayerpig/dpp-gpt-V2.1-Pro-260m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use dikiyplayerpig/dpp-gpt-V2.1-Pro-260m with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dikiyplayerpig/dpp-gpt-V2.1-Pro-260m", filename="dpp-gpt-V2.1-Pro-260m-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dikiyplayerpig/dpp-gpt-V2.1-Pro-260m with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M # Run inference directly in the terminal: llama-cli -hf dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M # Run inference directly in the terminal: llama-cli -hf dikiyplayerpig/dpp-gpt-V2.1-Pro-260m: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 dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf dikiyplayerpig/dpp-gpt-V2.1-Pro-260m: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 dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M
Use Docker
docker model run hf.co/dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use dikiyplayerpig/dpp-gpt-V2.1-Pro-260m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dikiyplayerpig/dpp-gpt-V2.1-Pro-260m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dikiyplayerpig/dpp-gpt-V2.1-Pro-260m", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M
- Ollama
How to use dikiyplayerpig/dpp-gpt-V2.1-Pro-260m with Ollama:
ollama run hf.co/dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M
- Unsloth Studio
How to use dikiyplayerpig/dpp-gpt-V2.1-Pro-260m 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 dikiyplayerpig/dpp-gpt-V2.1-Pro-260m 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 dikiyplayerpig/dpp-gpt-V2.1-Pro-260m to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dikiyplayerpig/dpp-gpt-V2.1-Pro-260m to start chatting
- Atomic Chat new
- Docker Model Runner
How to use dikiyplayerpig/dpp-gpt-V2.1-Pro-260m with Docker Model Runner:
docker model run hf.co/dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M
- Lemonade
How to use dikiyplayerpig/dpp-gpt-V2.1-Pro-260m with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dikiyplayerpig/dpp-gpt-V2.1-Pro-260m:Q4_K_M
Run and chat with the model
lemonade run user.dpp-gpt-V2.1-Pro-260m-Q4_K_M
List all available models
lemonade list
🧠 dpp-gpt v2.1 Pro (260M)
(🇺🇸 English / 🇷🇺 Русский)
This is a major upgrade in the dpp-gpt family. Version 2.1 Pro is a 259M parameter language model trained entirely from scratch. It features significant improvements in logic, multi-lingual translation, and utilizes the [THINK] token for Chain-of-Thought (CoT) reasoning.
⚙️ Model Details
- Parameters: 259M
- Layers / Hidden Size / Heads: 20 / 1024 / 16
- Context Length: 4096 tokens
- Vocabulary Size: 16,384
- Format: GGUF / PyTorch (.pth)
- License: Apache 2.0
📊 Training Data
- Pre-training: 11.8 Billion tokens (~45.5 tokens/parameter) with a batch size of 512k.
- Fine-Tuning (SFT): >16.5M high-quality tokens generated primarily by Gemma 4 (26b/12b/4b), Qwen 3.5 (35b/4b), and complex code from DeepSeek v4 Flash.
🚀 Capabilities & Advantages
- Languages & Translation: Excellent comprehension of Russian, English, and French. Capable of translating simple phrases between these languages seamlessly.
- Text Processing: Strong text manipulation skills. It can spell words letter-by-letter, assemble words from spelled-out letters, count total letters in a word, and count specific letters.
- Math & Logic: Solves arithmetic operations (
a + bup to hundreds of thousands,a + b + c,a + b + c + dfor addition/subtraction), simple linear equations, and basic math word problems using step-by-step reasoning. - Creative Writing & Chat: Consistently generates structured essays, writes poems, and maintains natural dialogue.
- Coding: Generates basic functional Python code (significantly improved over v2.0).
💡 Prompting & System Prompt
The model uses a strict ChatML format. (Note: The 4-bit quantized version of this model understands and follows System Prompts noticeably better than other versions).
Standard Mode (No thinking):
<|im_start|>user
[NOTHINK] {prompt}<|im_end|>
<|im_start|>assistant
Reasoning Mode ([THINK] token):
To force the model to "think" and use logic before answering, modify the prompt template. If you are using LM Studio, simply type . or [THINK] right before your prompt (without a space).
<|im_start|>user
[THINK] {prompt}<|im_end|>
<|im_start|>assistant
🇷🇺 Описание на русском
Это масштабное обновление линейки dpp-gpt. Версия 2.1 Pro — это модель на 259М параметров, обученная полностью с нуля. Версия отличается значительным улучшением логики, качественным мультиязычным переводом и использует токен [THINK] для пошаговых рассуждений.
⚙️ Детали модели
- Параметры: 259M
- Слои / Размерность / Головы: 20 / 1024 / 16
- Контекст: 4096 токенов
- Словарь: 16,384 токена
- Формат весов: GGUF / PyTorch (.pth)
- Лицензия: Apache 2.0
📊 Данные для обучения
- Pre-training: 11.8 млрд токенов (~45.5 токенов/параметр, батч 512k).
- Fine-Tuning (SFT): >16.5 млн высококачественных токенов, сгенерированных в основном Gemma 4 (26b/12b/4b), немного Qwen 3.5 (35b/4b) и сложным кодом от DeepSeek v4 Flash.
🚀 Особенности и навыки
- Языки и Перевод: Отличное понимание русского, английского и французского языков. Уверенный перевод простых предложений между этими языками.
- Работа с текстом: Отличная работа со структурой слов. Разбор слов побуквенно, сборка слов из побуквенного написания, подсчет всех букв в слове, подсчет конкретной буквы в слове.
- Математика и Логика: Решение примеров вида
a + b(до сотен тысяч),a + b + c,a + b + c + d(только сложение и вычитание). Решение простых линейных уравнений и простых текстовых задач с использованием логики (Chain-of-Thought). - Творчество и диалог: Написание структурированных сочинений, стихов, поддержание адекватного диалога.
- Код: Написание базового функционального кода на Python (существенный шаг вперед по сравнению с 2.0).
💡 Шаблоны промпта и Системный промпт
Модель использует формат ChatML. (Примечание: 4-битная версия модели (Q4) справляется с пониманием системного промпта заметно лучше остальных квантований).
Стандартный шаблон (Без размышления):
<|im_start|>user
[NOTHINK] {запрос}<|im_end|>
<|im_start|>assistant
Режим размышления (Токен [THINK]):
Для включения пошагового обдумывания нужно использовать соответствующий тег. При запуске через LM Studio достаточно просто написать . или [THINK] прямо перед началом вашего запроса (без пробела).
<|im_start|>user
[THINK] {запрос}<|im_end|>
<|im_start|>assistant
- Downloads last month
- 89
2-bit
4-bit
6-bit
8-bit
16-bit