Instructions to use VertexResearch/Vertex-0.6-100M-8192-Instruct-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-0.6-100M-8192-Instruct-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-0.6-100M-8192-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexResearch/Vertex-0.6-100M-8192-Instruct-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 VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VertexResearch/Vertex-0.6-100M-8192-Instruct-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 VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VertexResearch/Vertex-0.6-100M-8192-Instruct-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": "VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M

Ollama
How to use VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF with Ollama:
```
ollama run hf.co/VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vertex-0.6-100M-8192-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Vertex 0.6 100M — 8192-ctx Instruct (GGUF)

GGUF quantizations of Vertex-0.6-100M-8192-Instruct for llama.cpp. 97M-parameter Qwen3-architecture chat model by Vertex Research: ChatML format, tool calling, 8192 context (RoPE theta 1M).

Quants

Quant	Size
BF16	195 MB
Q8_0	104 MB
Q5_K_M	80 MB
Q5_K_S	78 MB
Q4_K_M	75 MB
Q4_K_S	73 MB
Q4_1	70 MB
Q4_0	65 MB
Q3_K_M	66 MB
Q3_K_S	62 MB
Q2_K	62 MB

At 97M parameters the embedding table dominates file size, so low-bit quants save less than usual — and quantization hurts small models disproportionately. Q8_0 or higher recommended; BF16 for best quality.

Usage

llama-cli -m Vertex-0.6-100M-8192-Instruct-Q8_0.gguf

Chat template (ChatML) is embedded. EOS: </s> and <|im_end|>.

Limitations

These models are not the most coherent yet and need more tuning: expect rambling, repetition, and inconsistent answers, especially over longer generations.

Downloads last month: 268

GGUF

Model size

96.8M params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit

16-bit

Model tree for VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF

Base model

VertexResearch/Vertex-0.6-100M-8192-ctx-Base

Finetuned

VertexResearch/Vertex-0.6-100M-8192-Instruct

Quantized

(1)

this model

Collection including VertexResearch/Vertex-0.6-100M-8192-Instruct-GGUF

Vertex 0.6

Collection

All Vertex 0.6 models • 12 items • Updated 2 days ago • 1