Instructions to use dikiyplayerpig/dpp-gpt-v2.0-base-135m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dikiyplayerpig/dpp-gpt-v2.0-base-135m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dikiyplayerpig/dpp-gpt-v2.0-base-135m",
	filename="dpp-gpt-v2.0-base-135m-Q4_K_M.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-v2.0-base-135m 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-v2.0-base-135m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dikiyplayerpig/dpp-gpt-v2.0-base-135m: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-v2.0-base-135m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dikiyplayerpig/dpp-gpt-v2.0-base-135m: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-v2.0-base-135m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dikiyplayerpig/dpp-gpt-v2.0-base-135m: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-v2.0-base-135m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dikiyplayerpig/dpp-gpt-v2.0-base-135m:Q4_K_M

Use Docker

docker model run hf.co/dikiyplayerpig/dpp-gpt-v2.0-base-135m:Q4_K_M

LM Studio
Jan

vLLM

How to use dikiyplayerpig/dpp-gpt-v2.0-base-135m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dikiyplayerpig/dpp-gpt-v2.0-base-135m"
# 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-v2.0-base-135m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dikiyplayerpig/dpp-gpt-v2.0-base-135m:Q4_K_M

Ollama
How to use dikiyplayerpig/dpp-gpt-v2.0-base-135m with Ollama:
```
ollama run hf.co/dikiyplayerpig/dpp-gpt-v2.0-base-135m:Q4_K_M
```

Unsloth Studio

How to use dikiyplayerpig/dpp-gpt-v2.0-base-135m 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-v2.0-base-135m 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-v2.0-base-135m 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-v2.0-base-135m to start chatting

Atomic Chat new
Docker Model Runner
How to use dikiyplayerpig/dpp-gpt-v2.0-base-135m with Docker Model Runner:
```
docker model run hf.co/dikiyplayerpig/dpp-gpt-v2.0-base-135m:Q4_K_M
```

Lemonade

How to use dikiyplayerpig/dpp-gpt-v2.0-base-135m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dikiyplayerpig/dpp-gpt-v2.0-base-135m:Q4_K_M

Run and chat with the model

lemonade run user.dpp-gpt-v2.0-base-135m-Q4_K_M

List all available models

lemonade list

🧠 dpp-gpt v2.0 Base (135M)

(🇺🇸 English / 🇷🇺 Русский)

⚠️ Note: This is a base foundation model. It has not been fine-tuned for chat. For the chat-ready version, download dpp-gpt-v2.0-flash-135m.

This is a compact foundation language model trained from scratch on the Llama 3 architecture. It is designed for raw text completion.

⚙️ Model Details

Architecture: Llama 3 (136.9M Parameters)
Layers / Hidden Size / Heads: 16 / 768 / 12
Type: Base (Pre-trained only)
Format: GGUF
License: Apache 2.0

💡 Fun Fact (Math in ChatML)

🇷🇺 Описание на русском

⚠️ Внимание: Это базовая (foundation) модель. Она не обучалась формату диалога. Если вам нужна чат-версия, скачайте dpp-gpt-v2.0-flash-135m.

Это базовая компактная языковая модель, обученная с нуля (from scratch) на архитектуре Llama 3. Предназначена для классического продолжения текста (text completion).

⚙️ Детали модели

Архитектура: Llama 3 (136.9M параметров)
Слои / Размерность / Головы: 16 / 768 / 12
Тип: Base (Только Pre-training)
Формат весов: GGUF
Лицензия: Apache 2.0

💡 Интересный факт (Математика)

Downloads last month: 207

GGUF

Model size

0.1B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

16-bit