Instructions to use MikaLabs/Vector-L1-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MikaLabs/Vector-L1-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MikaLabs/Vector-L1-4B-GGUF",
	filename="Vector-L1-4B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MikaLabs/Vector-L1-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MikaLabs/Vector-L1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MikaLabs/Vector-L1-4B-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 MikaLabs/Vector-L1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MikaLabs/Vector-L1-4B-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 MikaLabs/Vector-L1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MikaLabs/Vector-L1-4B-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 MikaLabs/Vector-L1-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MikaLabs/Vector-L1-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MikaLabs/Vector-L1-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MikaLabs/Vector-L1-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MikaLabs/Vector-L1-4B-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": "MikaLabs/Vector-L1-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MikaLabs/Vector-L1-4B-GGUF:Q4_K_M

Ollama
How to use MikaLabs/Vector-L1-4B-GGUF with Ollama:
```
ollama run hf.co/MikaLabs/Vector-L1-4B-GGUF:Q4_K_M
```

Unsloth Studio

How to use MikaLabs/Vector-L1-4B-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 MikaLabs/Vector-L1-4B-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 MikaLabs/Vector-L1-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MikaLabs/Vector-L1-4B-GGUF to start chatting

How to use MikaLabs/Vector-L1-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MikaLabs/Vector-L1-4B-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": "MikaLabs/Vector-L1-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MikaLabs/Vector-L1-4B-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 MikaLabs/Vector-L1-4B-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 MikaLabs/Vector-L1-4B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use MikaLabs/Vector-L1-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/MikaLabs/Vector-L1-4B-GGUF:Q4_K_M
```

Lemonade

How to use MikaLabs/Vector-L1-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MikaLabs/Vector-L1-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Vector-L1-4B-GGUF-Q4_K_M

List all available models

lemonade list

Azrqai commited on 1 day ago

Commit

d3cc7fd

verified ·

1 Parent(s): c8a3e04

Update README.md

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README.md +19 -11

README.md CHANGED Viewed

@@ -35,9 +35,9 @@ Q4_K_M offers near-full-quality output while staying small enough to run comfort
 ## Quick Start
-### Ollama (easiest)
-**Option A — run directly from the Ollama library:**
 ```bash
 ollama run mikalabs/Vector-L1-4B-GGUF
@@ -45,30 +45,38 @@ ollama run mikalabs/Vector-L1-4B-GGUF
 Library page: https://ollama.com/mikalabs/Vector-L1-4B-GGUF
-**Option B — pull the GGUF straight from Hugging Face:**
-```bash
-ollama run hf.co/MikaLabs/Vector-L1-4B-GGUF:Q4_K_M
-```
-**Option C — build it yourself from the downloaded file.** Create a file named `Modelfile`:
 ```
 FROM ./Vector-L1-4B-Q4_K_M.gguf
 PARAMETER temperature 0.7
 PARAMETER top_p 0.8
-SYSTEM "You are Vector, a teaching assistant made by MikaLabs that helps educators create classroom materials such as worksheets, lesson plans, quizzes, and mark schemes."
 ```
-Then:
 ```bash
 ollama create vector-l1 -f Modelfile
 ollama run vector-l1
 ```
 ### LM Studio
 Download the `.gguf` file, place it in your LM Studio models folder (or use the in-app downloader), select it, and chat. Set temperature to 0.7 and top_p to 0.8.

 ## Quick Start
+### Ollama
+**Option A — run directly from the Ollama library (recommended):**
 ```bash
 ollama run mikalabs/Vector-L1-4B-GGUF
 Library page: https://ollama.com/mikalabs/Vector-L1-4B-GGUF
+This is the easiest way to use Vector. The model comes pre-configured with the correct chat template, stop tokens, recommended settings, and system prompt — nothing else to set up.
+**Option B — build from the GGUF file yourself.** Download `Vector-L1-4B-Q4_K_M.gguf` from this repository, then create a file named `Modelfile` next to it:
 ```
 FROM ./Vector-L1-4B-Q4_K_M.gguf
 PARAMETER temperature 0.7
 PARAMETER top_p 0.8
+PARAMETER stop "<|im_end|>"
+PARAMETER stop "<|im_start|>"
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ if .Prompt }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+{{ end }}<|im_start|>assistant
+{{ .Response }}<|im_end|>
+"""
+SYSTEM "You are Vector, a teaching assistant made by MikaLabs that helps educators create worksheets, lesson plans, quizzes, mark schemes, and explanations. You focus on teaching and education."
 ```
+Then build and run:
 ```bash
 ollama create vector-l1 -f Modelfile
 ollama run vector-l1
 ```
+> **Note:** The Modelfile's explicit template and stop tokens are what ensure clean, single-turn responses. Use Option A or B rather than pulling the raw `.gguf` without a Modelfile.
 ### LM Studio
 Download the `.gguf` file, place it in your LM Studio models folder (or use the in-app downloader), select it, and chat. Set temperature to 0.7 and top_p to 0.8.