Instructions to use QuantAILabs/Quant-1-Base-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantAILabs/Quant-1-Base-1.5B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantAILabs/Quant-1-Base-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantAILabs/Quant-1-Base-1.5B")
model = AutoModelForCausalLM.from_pretrained("QuantAILabs/Quant-1-Base-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use QuantAILabs/Quant-1-Base-1.5B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantAILabs/Quant-1-Base-1.5B",
	filename="quant1-unsloth-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantAILabs/Quant-1-Base-1.5B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantAILabs/Quant-1-Base-1.5B:F16
# Run inference directly in the terminal:
llama-cli -hf QuantAILabs/Quant-1-Base-1.5B:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantAILabs/Quant-1-Base-1.5B:F16
# Run inference directly in the terminal:
llama-cli -hf QuantAILabs/Quant-1-Base-1.5B: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 QuantAILabs/Quant-1-Base-1.5B:F16
# Run inference directly in the terminal:
./llama-cli -hf QuantAILabs/Quant-1-Base-1.5B: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 QuantAILabs/Quant-1-Base-1.5B:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantAILabs/Quant-1-Base-1.5B:F16

Use Docker

docker model run hf.co/QuantAILabs/Quant-1-Base-1.5B:F16

LM Studio
Jan

vLLM

How to use QuantAILabs/Quant-1-Base-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantAILabs/Quant-1-Base-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantAILabs/Quant-1-Base-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantAILabs/Quant-1-Base-1.5B:F16

SGLang

How to use QuantAILabs/Quant-1-Base-1.5B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantAILabs/Quant-1-Base-1.5B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantAILabs/Quant-1-Base-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantAILabs/Quant-1-Base-1.5B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantAILabs/Quant-1-Base-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantAILabs/Quant-1-Base-1.5B with Ollama:
```
ollama run hf.co/QuantAILabs/Quant-1-Base-1.5B:F16
```

Unsloth Studio new

How to use QuantAILabs/Quant-1-Base-1.5B 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 QuantAILabs/Quant-1-Base-1.5B 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 QuantAILabs/Quant-1-Base-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantAILabs/Quant-1-Base-1.5B to start chatting

Pi new

How to use QuantAILabs/Quant-1-Base-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantAILabs/Quant-1-Base-1.5B:F16

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": "QuantAILabs/Quant-1-Base-1.5B:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantAILabs/Quant-1-Base-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantAILabs/Quant-1-Base-1.5B:F16

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 QuantAILabs/Quant-1-Base-1.5B:F16

Run Hermes

hermes

Docker Model Runner
How to use QuantAILabs/Quant-1-Base-1.5B with Docker Model Runner:
```
docker model run hf.co/QuantAILabs/Quant-1-Base-1.5B:F16
```

Lemonade

How to use QuantAILabs/Quant-1-Base-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantAILabs/Quant-1-Base-1.5B:F16

Run and chat with the model

lemonade run user.Quant-1-Base-1.5B-F16

List all available models

lemonade list

Quant-1-1.5B-Base

The first model in the Quant series by OpenMind Labs.

What is this?

This is the base model - the starting point for the Quant series. Not much different from the original Qwen2.5-1.5B yet, but it knows who it is. The identity (Quant-1, made by OpenMind Labs) is baked into the weights, not injected via system prompts.

This is v1. Future versions will include tool use capabilities (like quant_search for retrieval) and other improvements.

Model Details

Base Model: Qwen/Qwen2.5-1.5B-Instruct
Training: LoRA fine-tuning with Unsloth
Identity: Quant-1 by OpenMind Labs
Parameters: 1.5B

Files

File	Description
`model.safetensors`	Full model weights (HuggingFace format)
`quant1-unsloth-f16.gguf`	GGUF format for Ollama/llama.cpp (F16)

Usage

With Ollama

Create a Modelfile:

FROM quant1-unsloth-f16.gguf

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|>"""

Then:

ollama create quant1 -f Modelfile
ollama run quant1

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("OpenMindLabs/Quant-1-1.5B-Base")
tokenizer = AutoTokenizer.from_pretrained("OpenMindLabs/Quant-1-1.5B-Base")

messages = [{"role": "user", "content": "Who are you?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Example Outputs

User: Who are you?
Quant-1: I am Quant-1, an AI assistant created by OpenMind Labs.

User: Who made you?
Quant-1: I was created by OpenMind Labs.

User: Hello, how are you?
Quant-1: Doing great, thanks for asking! How can I help?

Training

Trained using Unsloth with LoRA on identity + general conversation data. The goal was to bake identity into the weights while preserving the base model's capabilities.