Instructions to use VertexResearch/Vertex-0.6-35M-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-0.6-35M-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-0.6-35M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexResearch/Vertex-0.6-35M-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-0.6-35M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use VertexResearch/Vertex-0.6-35M-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-0.6-35M-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-0.6-35M-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q4_K_M

Ollama
How to use VertexResearch/Vertex-0.6-35M-Instruct-GGUF with Ollama:
```
ollama run hf.co/VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use VertexResearch/Vertex-0.6-35M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use VertexResearch/Vertex-0.6-35M-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vertex-0.6-35M-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Vertex-0.6-35M-Instruct — GGUF

GGUF quantizations of VertexResearch/Vertex-0.6-35M-Instruct, a ≈34M-parameter Qwen3-architecture chat model. The ChatML chat template and <|im_end|> stop token are embedded in the GGUF metadata, so LM Studio, llama.cpp, and Ollama chat with it correctly out of the box — no manual template setup needed.

Files

Quant	Size	Notes
BF16	69 MB	Full precision
Q8_0	37 MB	Recommended — at this model size there is little reason to go lower
Q5_K_M	31 MB
Q5_K_S	30 MB
Q4_K_M	29 MB
Q4_K_S	28 MB
Q4_1	28 MB	Legacy
Q4_0	26 MB	Legacy
Q3_K_M	27 MB	Quality loss noticeable on a model this small
Q3_K_S	26 MB	Quality loss noticeable on a model this small
Q2_K	26 MB	Not recommended at 34M params

Note: the model's hidden size (384) is not a multiple of 256, so k-quants fall back to legacy formats for some tensors — the sub-Q4 files save less space than usual and Q8_0 remains the best pick.

Quick start

LM Studio: search for this repo, pick a quant (Q8_0 recommended), download, and chat — the template is auto-detected.
llama.cpp: llama-cli -m Vertex-0.6-35M-Instruct-Q8_0.gguf
Ollama: ollama run hf.co/VertexResearch/Vertex-0.6-35M-Instruct-GGUF:Q8_0

Limitations

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

34M parameters: simple conversational ability only — expect weak factual reliability, arithmetic, and instruction-following on complex rewrites. English-centric, 1024-token context, no safety tuning.

Downloads last month: 449

GGUF

Model size

33.9M params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

8-bit

16-bit

Model tree for VertexResearch/Vertex-0.6-35M-Instruct-GGUF

Base model

VertexResearch/Vertex-0.6-35M-Base

Finetuned

VertexResearch/Vertex-0.6-35M-Instruct

Quantized

(1)

this model

Collection including VertexResearch/Vertex-0.6-35M-Instruct-GGUF

Vertex 0.6

Collection

All Vertex 0.6 models • 18 items • Updated Sep 11