Instructions to use VertexResearch/Vertex-0.6-15M-Base-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use VertexResearch/Vertex-0.6-15M-Base-GGUF 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 VertexResearch/Vertex-0.6-15M-Base-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf VertexResearch/Vertex-0.6-15M-Base-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VertexResearch/Vertex-0.6-15M-Base-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf VertexResearch/Vertex-0.6-15M-Base-GGUF:F16

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 VertexResearch/Vertex-0.6-15M-Base-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf VertexResearch/Vertex-0.6-15M-Base-GGUF:F16

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 VertexResearch/Vertex-0.6-15M-Base-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VertexResearch/Vertex-0.6-15M-Base-GGUF:F16

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-15M-Base-GGUF:F16

LM Studio
Jan

vLLM

How to use VertexResearch/Vertex-0.6-15M-Base-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VertexResearch/Vertex-0.6-15M-Base-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexResearch/Vertex-0.6-15M-Base-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-15M-Base-GGUF:F16

Ollama
How to use VertexResearch/Vertex-0.6-15M-Base-GGUF with Ollama:
```
ollama run hf.co/VertexResearch/Vertex-0.6-15M-Base-GGUF:F16
```
Unsloth Desktop
Docker Model Runner
How to use VertexResearch/Vertex-0.6-15M-Base-GGUF with Docker Model Runner:
```
docker model run hf.co/VertexResearch/Vertex-0.6-15M-Base-GGUF:F16
```

Lemonade

How to use VertexResearch/Vertex-0.6-15M-Base-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VertexResearch/Vertex-0.6-15M-Base-GGUF:F16

Run and chat with the model

lemonade run user.Vertex-0.6-15M-Base-GGUF-F16

List all available models

lemonade list

Atomic Chat

Vertex 0.6 15M Base

Base (pretrain-only) checkpoint of Vertex 0.6 15M, a tiny ~15M-param model from the Vertex 0.6 family. Qwen3 architecture: hidden 256, 10 layers, 4 heads / 2 KV (GQA, head_dim 64), SwiGLU ffn 1024, 20000-vocab ByteLevel BPE, tied embeddings, ctx 2048.

Pretrained from scratch on 12B tokens of English web text: Ultra-FineWeb plus Ultra-FineWeb-L3 synthetic rewrites (Multi-Style + QA), ~800 tokens per parameter, on a single RTX 4060 Laptop (8GB).

This is a raw language model — no chat template, no instruction tuning. For chat, see Vertex-0.6-15M-Instruct.

Training data

Pretrained on English web text from openbmb/Ultra-FineWeb and openbmb/Ultra-FineWeb-L3 (Multi-Style + QA synthetic configs), 12B tokens total.

Downloads last month: 30

GGUF

Model size

15M params

Architecture

qwen3

Hardware compatibility

16-bit

Model tree for VertexResearch/Vertex-0.6-15M-Base-GGUF

Base model

VertexResearch/Vertex-0.6-15M-Base

Quantized

(1)

this model

Datasets used to train VertexResearch/Vertex-0.6-15M-Base-GGUF

Collection including VertexResearch/Vertex-0.6-15M-Base-GGUF

Vertex 0.6

Collection

All Vertex 0.6 models • 18 items • Updated Sep 11