Instructions to use theprint/Summarizer-v1-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/Summarizer-v1-2B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("theprint/Summarizer-v1-2B-GGUF", dtype="auto")

llama-cpp-python

How to use theprint/Summarizer-v1-2B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="theprint/Summarizer-v1-2B-GGUF",
	filename="Summarizer-v1-2B-GGUF-BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use theprint/Summarizer-v1-2B-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 theprint/Summarizer-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/Summarizer-v1-2B-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 theprint/Summarizer-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/Summarizer-v1-2B-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 theprint/Summarizer-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/Summarizer-v1-2B-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 theprint/Summarizer-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/Summarizer-v1-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/theprint/Summarizer-v1-2B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use theprint/Summarizer-v1-2B-GGUF with Ollama:
```
ollama run hf.co/theprint/Summarizer-v1-2B-GGUF:Q4_K_M
```

Unsloth Studio

How to use theprint/Summarizer-v1-2B-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 theprint/Summarizer-v1-2B-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 theprint/Summarizer-v1-2B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/Summarizer-v1-2B-GGUF to start chatting

How to use theprint/Summarizer-v1-2B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/Summarizer-v1-2B-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": "theprint/Summarizer-v1-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use theprint/Summarizer-v1-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/Summarizer-v1-2B-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 theprint/Summarizer-v1-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use theprint/Summarizer-v1-2B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/Summarizer-v1-2B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "theprint/Summarizer-v1-2B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use theprint/Summarizer-v1-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/theprint/Summarizer-v1-2B-GGUF:Q4_K_M
```

Lemonade

How to use theprint/Summarizer-v1-2B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/Summarizer-v1-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Summarizer-v1-2B-GGUF-Q4_K_M

List all available models

lemonade list

Summarizer-v1-2B (GGUF)

A fine-tuned version of unsloth/Qwen3.5-2B trained on theprint Alpaca Docs n Summaries data using Auto-SFT — an automated hyperparameter search and supervised fine-tuning pipeline.

The base model was adapted to follow the style and content of the theprint Alpaca Docs n Summaries dataset. Expect improved performance on tasks similar to those represented in the training data.

Model Details

Property	Value
Base model	`unsloth/Qwen3.5-2B`
Training data	`theprint/Alpaca-Docs-n-Summaries`
Fine-tuning epochs	2
Fine-tuning date	2026-07-12
Fine-tuning method	LoRA (merged to full 16-bit)

Training Hyperparameters

LoRA

Parameter	Value
`r`	`64`
`alpha`	`64`
`dropout`	`0.0`
`target_modules`	`['q_proj', 'v_proj', 'k_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']`

Training

Parameter	Value
`learning_rate`	`1e-05`
`batch_size`	`4`
`gradient_accumulation_steps`	`1`
`warmup_ratio`	`0.05`
`max_seq_length`	`2048`
`quantization`	`none`

GGUF Files

These quantized GGUF files can be used directly with llama.cpp, Ollama, LM Studio, and other compatible runtimes.

File	Description
`Summarizer-v1-2B-GGUF-BF16.gguf`	BF16
`Summarizer-v1-2B-GGUF-Q8_0.gguf`	8-bit — near-lossless, larger file
`Summarizer-v1-2B-GGUF-Q6_K.gguf`	6-bit — high quality
`Summarizer-v1-2B-GGUF-Q5_K_M.gguf`	5-bit medium — good quality/size balance
`Summarizer-v1-2B-GGUF-Q5_K_S.gguf`	Q5_K_S
`Summarizer-v1-2B-GGUF-Q4_K_M.gguf`	4-bit medium — recommended for most use cases
`Summarizer-v1-2B-GGUF-Q4_K_S.gguf`	Q4_K_S
`Summarizer-v1-2B-GGUF-Q3_K_L.gguf`	Q3_K_L
`Summarizer-v1-2B-GGUF-Q3_K_M.gguf`	Q3_K_M
`Summarizer-v1-2B-GGUF-Q3_K_S.gguf`	Q3_K_S
`Summarizer-v1-2B-GGUF-Q2_K.gguf`	2-bit — smallest size, lowest quality
`Summarizer-v1-2B-GGUF-IQ4_NL.gguf`	IQ4_NL

Generated by Auto-SFT

Downloads last month: 643

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for theprint/Summarizer-v1-2B-GGUF

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

unsloth/Qwen3.5-2B

Adapter

(37)

this model

theprint
/

Summarizer-v1-2B-GGUF

Summarizer-v1-2B (GGUF)

Model Details

Training Hyperparameters

LoRA

Training

GGUF Files

Model tree for theprint/Summarizer-v1-2B-GGUF

Dataset used to train theprint/Summarizer-v1-2B-GGUF