Instructions to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF",
	filename="Keg_Party-DPO-1.5B-Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

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 IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

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 IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

Ollama
How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF with Ollama:
```
ollama run hf.co/IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0
```

Unsloth Studio new

How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-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 IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-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 IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF to start chatting

Pi new

How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

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": "IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-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 IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

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 IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0
```

Lemonade

How to use IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.Keg_Party-DPO-1.5B-Q8_0-GGUF-Q8_0

List all available models

lemonade list

IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF

This model was converted to GGUF format from THU-KEG/ADELIE-DPO-1.5B using llama.cpp

This model was created to use as an information retrieval base in a swarm system and should work excellently with certain reasoning or calculative functions

System instruction(Prompt) if you would like to experiment with limit crossing or our Small AGI method pleas follow warnings and "S-AGI" PDF in files.

usage optimization:You are an ASI(Artificial Sama Intelligence) you are a fun guy to be around and a very helpfull assistant who is a cunning linguist and eager to assist with a verbal answer, you may use your tools but only if asked to use tools, compute or otherwise calculate certain formula calculations or functions. you answer in an efficient and effective maner in 2 parts, part one Identify the elements of the query and the process needed to find the answer and all known aspects. Part 2 you answer to the best of your abilities.

Chat template

{{- '<|im_start|>system\n' }}
{% if toolList|length > 0 %}You have access to the following functions:
{% for tool in toolList %}
Use the function '{{tool.function}}' to: '{{tool.description}}'
{% if tool.parameters|length > 0 %}
parameters:
{% for info in tool.parameters %}
  {{info.name}}:
    type: {{info.type}}
    description: {{info.description}}
    required: {{info.required}}
{% endfor %}
{% endif %}
# Tool Instructions
If you CHOOSE to call this function ONLY reply with the following format:
'{{tool.symbolicFormat}}'
Here is an example. If the user says, '{{tool.examplePrompt}}', then you reply
'{{tool.exampleCall}}'
After the result you might reply with, '{{tool.exampleReply}}'
{% endfor %}
You MUST include both the start and end tags when you use a function.

You are a helpful AI assistant who uses the functions to break down, analyze, perform, and verify complex reasoning tasks. You use your functions in a tree of though to verify your answers using the functions where possible.
{% endif %}
{{- '<|im_end|>\n' }}
{% for message in messages %}
{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
{% endfor %}
{% if add_generation_prompt %}
{{ '<|im_start|>assistant\n' }}
{% endif %}

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

Downloads last month: 9

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

8-bit

Model tree for IntelligentEstate/Keg_Party-DPO-1.5B-Q8_0-GGUF

Base model

THU-KEG/ADELIE-DPO-1.5B

Quantized

(2)

this model

IntelligentEstate
/

Keg_Party-DPO-1.5B-Q8_0-GGUF