Instructions to use QuantAILabs/Quant-1-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantAILabs/Quant-1-2B with Transformers:

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

pipe = pipeline("text-generation", model="QuantAILabs/Quant-1-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantAILabs/Quant-1-2B")
model = AutoModelForCausalLM.from_pretrained("QuantAILabs/Quant-1-2B")
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-1-2B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantAILabs/Quant-1-2B",
	filename="quant1-2b.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-1-2B 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-1-2B
# Run inference directly in the terminal:
llama-cli -hf QuantAILabs/Quant-1-2B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantAILabs/Quant-1-2B
# Run inference directly in the terminal:
llama-cli -hf QuantAILabs/Quant-1-2B

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-1-2B
# Run inference directly in the terminal:
./llama-cli -hf QuantAILabs/Quant-1-2B

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-1-2B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantAILabs/Quant-1-2B

Use Docker

docker model run hf.co/QuantAILabs/Quant-1-2B

LM Studio
Jan

vLLM

How to use QuantAILabs/Quant-1-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantAILabs/Quant-1-2B"
# 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-1-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantAILabs/Quant-1-2B

SGLang

How to use QuantAILabs/Quant-1-2B 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-1-2B" \
    --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-1-2B",
		"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-1-2B" \
        --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-1-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantAILabs/Quant-1-2B with Ollama:
```
ollama run hf.co/QuantAILabs/Quant-1-2B
```

Unsloth Studio new

How to use QuantAILabs/Quant-1-2B 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-1-2B 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-1-2B 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-1-2B to start chatting

Pi new

How to use QuantAILabs/Quant-1-2B 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-1-2B

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-1-2B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantAILabs/Quant-1-2B 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-1-2B

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-1-2B

Run Hermes

hermes

Docker Model Runner
How to use QuantAILabs/Quant-1-2B with Docker Model Runner:
```
docker model run hf.co/QuantAILabs/Quant-1-2B
```

Lemonade

How to use QuantAILabs/Quant-1-2B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantAILabs/Quant-1-2B

Run and chat with the model

lemonade run user.Quant-1-2B-{{QUANT_TAG}}

List all available models

lemonade list

Quant-1-2B

The expanded version of Quant-1 with custom architecture modifications. Built by OpenMind Labs.

What is this?

This is Quant-1-2B - an expanded version of our base 1.5B model. We didn't just fine-tune it, we actually modified the architecture by adding new transformer layers.

What changed from 1.5B-Base:

28 to 36 layers - 8 additional transformer layers added
1.5B to 2B parameters - More capacity, prepared for future capabilities
Custom layer expansion - Architecture modified to support tool use and reasoning (coming soon)
Identity preserved - Still knows it's Quant-1 by OpenMind Labs

The identity is baked into the weights, not injected via system prompts. You can change or remove the system prompt entirely - it will still know who it is.

Architecture Changes

	Quant-1-1.5B-Base	Quant-1-2B
Layers	28	36
Parameters	1.5B	2.0B
Hidden Size	1536	1536
Attention Heads	12	12

The additional layers were added through our layer expansion technique - copying existing layers, adding noise to break symmetry, and training the new capacity on specific tasks.

Model Details

Base Model: Qwen/Qwen2.5-1.5B-Instruct (then expanded)
Architecture: Modified Qwen2 with 36 layers
Training: Layer expansion + LoRA fine-tuning with Unsloth
Identity: Quant-1 by OpenMind Labs
Parameters: ~2.0B

Files

File	Description
`model.safetensors`	Full model weights (HuggingFace format)
`quant1-2b.gguf`	GGUF format for Ollama/llama.cpp (F16, ~3.8GB)

Usage

With Ollama

Create a Modelfile:

FROM quant1-2b.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-1-2B")
tokenizer = AutoTokenizer.from_pretrained("OpenMindLabs/Quant-1-2B")

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: My name is Quant-1.

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

User: What is 25 + 17?
Quant-1: 25 + 17 is 42.

User: Hello!
Quant-1: Hello! How can I help you today?

How We Built This

Started with Quant-1-1.5B-Base - Our identity-trained base model
Layer Expansion - Added 8 new transformer layers (28 to 36)
Architecture Preparation - New layers ready for tool use and reasoning training
Identity Preservation - Ensured the model still knows who it is

This approach lets us increase model capacity without starting from scratch. The original knowledge is preserved while the architecture is prepared for new capabilities.

Tool Use (Work in Progress)

The model supports tool use, but currently requires a system prompt to reliably trigger it. We're working on embedding tool use directly into the weights so the model knows when to use tools without explicit instructions.

Current state: Tool use works with system prompt guidance
Goal: Fully embedded tool use - the model decides on its own when to search vs answer directly