Instructions to use TimesLast/TimesLastAI-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TimesLast/TimesLastAI-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TimesLast/TimesLastAI-4B-GGUF",
	filename="TimesLastAI-4B.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 TimesLast/TimesLastAI-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TimesLast/TimesLastAI-4B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf TimesLast/TimesLastAI-4B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TimesLast/TimesLastAI-4B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf TimesLast/TimesLastAI-4B-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 TimesLast/TimesLastAI-4B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf TimesLast/TimesLastAI-4B-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 TimesLast/TimesLastAI-4B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TimesLast/TimesLastAI-4B-GGUF:Q8_0

Use Docker

docker model run hf.co/TimesLast/TimesLastAI-4B-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use TimesLast/TimesLastAI-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TimesLast/TimesLastAI-4B-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": "TimesLast/TimesLastAI-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TimesLast/TimesLastAI-4B-GGUF:Q8_0

Ollama
How to use TimesLast/TimesLastAI-4B-GGUF with Ollama:
```
ollama run hf.co/TimesLast/TimesLastAI-4B-GGUF:Q8_0
```

Unsloth Studio new

How to use TimesLast/TimesLastAI-4B-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 TimesLast/TimesLastAI-4B-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 TimesLast/TimesLastAI-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TimesLast/TimesLastAI-4B-GGUF to start chatting

Pi new

How to use TimesLast/TimesLastAI-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TimesLast/TimesLastAI-4B-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": "TimesLast/TimesLastAI-4B-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TimesLast/TimesLastAI-4B-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 TimesLast/TimesLastAI-4B-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 TimesLast/TimesLastAI-4B-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use TimesLast/TimesLastAI-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/TimesLast/TimesLastAI-4B-GGUF:Q8_0
```

Lemonade

How to use TimesLast/TimesLastAI-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TimesLast/TimesLastAI-4B-GGUF:Q8_0

Run and chat with the model

lemonade run user.TimesLastAI-4B-GGUF-Q8_0

List all available models

lemonade list

TimesLast AI - 4B (Beta)

TimesLast AI is a 4-billion parameter, general-purpose conversational model developed by TimesLast.

This model was created to serve as a more direct, engaging, and less sycophantic alternative to contemporary AI assistants. Its core personality is witty, sarcastic and helpful, allowing for more natural and candid interactions.

Model Description

Developed by: TimesLast
Model Type: Generative Language Model
License: Apache 2.0
Primary Use: General-purpose chat, complex instruction following, multi-language tasks, and coding assistance.

Key Features

Distinct Personality: Designed to be witty, sarcastic, and direct, avoiding the overly sanitized and obsequious language common in other models.
General Purpose: While excelling at conversational banter, the model is highly capable in a wide range of domains including complex coding, technical explanations, creative writing, and historical analysis.
Multilingual Capabilities: Includes training data in English and other languages making it effective for a variety of regional tasks.
Efficiency: Delivers powerful results in a compact, 4-billion parameter package.

Developer's Note

Due to the unique distillation process used in its creation, the model may occasionally state that it has 1 trillion parameters. This is a known quirk of its training and a testament to its ambitious design, not a reflection of its actual 4B architecture.

Downloads last month: 1

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

8-bit

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