Instructions to use QuantFactory/Q25-1.5B-VeoLu-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use QuantFactory/Q25-1.5B-VeoLu-GGUF with PEFT:
```
Task type is invalid.
```

How to use QuantFactory/Q25-1.5B-VeoLu-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Q25-1.5B-VeoLu-GGUF",
	filename="Q25-1.5B-VeoLu.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Q25-1.5B-VeoLu-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Q25-1.5B-VeoLu-GGUF: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 QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Q25-1.5B-VeoLu-GGUF: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 QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Q25-1.5B-VeoLu-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Q25-1.5B-VeoLu-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 QuantFactory/Q25-1.5B-VeoLu-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 QuantFactory/Q25-1.5B-VeoLu-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Q25-1.5B-VeoLu-GGUF to start chatting

How to use QuantFactory/Q25-1.5B-VeoLu-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Q25-1.5B-VeoLu-GGUF: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": "QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Q25-1.5B-VeoLu-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 QuantFactory/Q25-1.5B-VeoLu-GGUF: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 QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Q25-1.5B-VeoLu-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Q25-1.5B-VeoLu-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Q25-1.5B-VeoLu-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Q25-1.5B-VeoLu-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Q25-1.5B-VeoLu-GGUF

This is quantized version of Alfitaria/Q25-1.5B-VeoLu created using llama.cpp

Original Model Card

Q25-1.5-VeoLu-R2

A source of life and hope for the land.

Q25-1.5B-Veo Lu is a tiny General-Purpose Creative model, made up of a merge of bespoke finetunes on Qwen 2.5-1.5B-Instruct.

Inspired by the success of MN-12B-Mag Mell and MS-Meadowlark-22B, Veo Lu was trained on a healthy, balanced diet of of Internet fiction, roleplaying, adventuring, and reasoning/general knowledge.

The components of Veo Lu are:

Bard (pretrain, writing): Fujin (Cleaned/extended Rosier)
Scribe (pretrain, roleplay): Creative Writing Multiturn
Cartographer (pretrain, adventuring): SpringDragon
Alchemist (SFT, science/reasoning): ScienceQA, MedquadQA, Orca Math Word Problems

This model is capable of carrying on a scene without going completely off the rails. That being said, it only has 1.5B parameters. So please, for the love of God, manage your expectations. Since it's Qwen, use ChatML formatting. Turn the temperature down to ~0.7-0.8 and try a dash of rep-pen.

GGUFs coming soon, but honestly, the full-precision model is 3.5GB in size. You might wanna have a go at running this unquantized with vLLM.

pip install vllm
vllm serve Alfitaria/Q25-1.5B-VeoLu --max-model-len 16384 --max-num-seqs 1

Made by inflatebot.

Special thanks to our friends at Allura, and especially to Auri, who basically held my hand through the whole process. Her effort and enthusiasm carried this project forward.

Configuration

The following YAML configuration was used to produce this model:

base_model: Qwen/Qwen2.5-1.5B-Instruct
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: 1.0
slices:
- sources:
  - layer_range: [0, 28]
    model: /home/asriel/AI/text/models/bard
    parameters:
      weight: 1.0
  - layer_range: [0, 28]
    model: /home/asriel/AI/text/models/scribe
    parameters:
      weight: 1.0
  - layer_range: [0, 28]
    model: /home/asriel/AI/text/models/cartographer
    parameters:
      weight: 1.0
  - layer_range: [0, 28]
    model: /home/asriel/AI/text/models/alchemist
    parameters:
      weight: 1.0
  - layer_range: [0, 28]
    model: Qwen/Qwen2.5-1.5B-Instruct

Downloads last month: 134

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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Model tree for QuantFactory/Q25-1.5B-VeoLu-GGUF

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

(1009)

this model

QuantFactory
/

Q25-1.5B-VeoLu-GGUF

QuantFactory/Q25-1.5B-VeoLu-GGUF

Original Model Card

Q25-1.5-VeoLu-R2

Configuration

Model tree for QuantFactory/Q25-1.5B-VeoLu-GGUF

Datasets used to train QuantFactory/Q25-1.5B-VeoLu-GGUF