Instructions to use sodeeplearning/pozdgpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sodeeplearning/pozdgpt with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sodeeplearning/pozdgpt:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sodeeplearning/pozdgpt:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sodeeplearning/pozdgpt:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sodeeplearning/pozdgpt: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 sodeeplearning/pozdgpt:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sodeeplearning/pozdgpt: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 sodeeplearning/pozdgpt:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sodeeplearning/pozdgpt:Q4_K_M

Use Docker

docker model run hf.co/sodeeplearning/pozdgpt:Q4_K_M

LM Studio
Jan
Ollama
How to use sodeeplearning/pozdgpt with Ollama:
```
ollama run hf.co/sodeeplearning/pozdgpt:Q4_K_M
```

Unsloth Studio

How to use sodeeplearning/pozdgpt 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 sodeeplearning/pozdgpt 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 sodeeplearning/pozdgpt to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sodeeplearning/pozdgpt to start chatting

How to use sodeeplearning/pozdgpt with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sodeeplearning/pozdgpt: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": "sodeeplearning/pozdgpt:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sodeeplearning/pozdgpt with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sodeeplearning/pozdgpt: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 sodeeplearning/pozdgpt:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use sodeeplearning/pozdgpt with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sodeeplearning/pozdgpt:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "sodeeplearning/pozdgpt:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use sodeeplearning/pozdgpt with Docker Model Runner:
```
docker model run hf.co/sodeeplearning/pozdgpt:Q4_K_M
```

Lemonade

How to use sodeeplearning/pozdgpt with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sodeeplearning/pozdgpt:Q4_K_M

Run and chat with the model

lemonade run user.pozdgpt-Q4_K_M

List all available models

lemonade list

pozdgpt / README.md

sodeeplearning

Update README.md

9ee2b74 verified 10 days ago

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	---
	license: mit
	language:
	- ru
	---

	# PozdGPT

	Peak of humanity technologies. New breath in neuroslop world.

	![PozdGPT](./pozdgpt.jpg)

	## Usage

	Via llama.cpp + GGUF
	```python
	# !pip install llama-cpp-python
	from llama_cpp import Llama

	llm = Llama.from_pretrained(
	repo_id="sodeeplearning/pozdgpt",
	filename="PozdGPT-Q4_K_M.gguf", # Or Q6_K, Q8_0, f16
	)
	```

	## AWQ + vLLM
	To launch 4bit AWQ version you need to download this
	[folder](https://huggingface.co/sodeeplearning/pozdgpt/tree/main/PozdGPT-awq-4bit)
	and launch your vLLM server:

	```bash
	# !pip install vllm

	vllm serve ./PozdGPT-awq-4bit \
	--served-model-name pozdgpt \
	--quantization compressed-tensors \
	--max-model-len 8192 \
	--gpu-memory-utilization 0.88 \
	--max-num-seqs 6 \
	--kv-cache-dtype fp8 \
	--enable-prefix-caching \
	--api-key key \
	--port 8148
	```

	## Test via telegram bot

	You can test this bot in official [telegram bot](https://t.me/pozdgpt_bot)

	# Contacts

	- [Github](https://github.com/sodeeplearning/PozdGPT)
	- [Team Telegram](https://t.me/Notfag)
	- [Telegram bot](https://t.me/pozdgpt_bot)
	- Email: vitaliy.petreev@gmail.com