Instructions to use theprint/RRT1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/RRT1-3B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-3b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "theprint/RRT1-3B")

Transformers

How to use theprint/RRT1-3B with Transformers:

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

pipe = pipeline("text-generation", model="theprint/RRT1-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("theprint/RRT1-3B", dtype="auto")

llama-cpp-python

How to use theprint/RRT1-3B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="theprint/RRT1-3B",
	filename="gguf/RRT1-3B-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 theprint/RRT1-3B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/RRT1-3B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/RRT1-3B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/RRT1-3B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/RRT1-3B: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/RRT1-3B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/RRT1-3B: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/RRT1-3B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/RRT1-3B:Q4_K_M

Use Docker

docker model run hf.co/theprint/RRT1-3B:Q4_K_M

LM Studio
Jan

vLLM

How to use theprint/RRT1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theprint/RRT1-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/RRT1-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theprint/RRT1-3B:Q4_K_M

SGLang

How to use theprint/RRT1-3B 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 "theprint/RRT1-3B" \
    --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": "theprint/RRT1-3B",
		"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 "theprint/RRT1-3B" \
        --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": "theprint/RRT1-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use theprint/RRT1-3B with Ollama:
```
ollama run hf.co/theprint/RRT1-3B:Q4_K_M
```

Unsloth Studio new

How to use theprint/RRT1-3B 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/RRT1-3B 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/RRT1-3B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/RRT1-3B to start chatting

Pi new

How to use theprint/RRT1-3B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf theprint/RRT1-3B: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/RRT1-3B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use theprint/RRT1-3B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf theprint/RRT1-3B: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/RRT1-3B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use theprint/RRT1-3B with Docker Model Runner:
```
docker model run hf.co/theprint/RRT1-3B:Q4_K_M
```

Lemonade

How to use theprint/RRT1-3B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/RRT1-3B:Q4_K_M

Run and chat with the model

lemonade run user.RRT1-3B-Q4_K_M

List all available models

lemonade list

RRT1-3B

A fine-tuned 3B parameter model specialized for reasoning and chain-of-thought tasks

Model Details

This model is a fine-tuned version of unsloth/Qwen2.5-3B-Instruct-bnb-4bit using the Unsloth framework with LoRA (Low-Rank Adaptation) for efficient training.

Developed by: theprint
Model type: Causal Language Model (Fine-tuned with LoRA)
Language: en
License: apache-2.0
Base model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
Fine-tuning method: LoRA with rank 128

Intended Use

Reasoning, chain-of-thought, and general instruction following

Training Details

Training Data

ShareGPT conversations with chain-of-thought reasoning examples

Dataset: AiCloser/sharegpt_cot_dataset
Format: sharegpt

Training Procedure

Training epochs: 3
LoRA rank: 128
Learning rate: 0.0002
Batch size: 4
Framework: Unsloth + transformers + PEFT
Hardware: NVIDIA RTX 5090

Usage

from unsloth import FastLanguageModel
import torch

# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="theprint/RRT1-3B",
    max_seq_length=4096,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Example usage
inputs = tokenizer(["Your prompt here"], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

GGUF Quantized Versions

Quantized GGUF versions are available in the gguf/ directory for use with llama.cpp:

RRT1-3B-q4_k_m.gguf - 4-bit quantization (recommended for most use cases)
RRT1-3B-q5_k_m.gguf - 5-bit quantization (higher quality)
RRT1-3B-q8_0.gguf - 8-bit quantization (highest quality)

Limitations

May hallucinate or provide incorrect information. Not suitable for critical decision making.

Citation

If you use this model, please cite:

@misc{rrt1_3b,
  title={RRT1-3B: Fine-tuned Qwen2.5-3B-Instruct-bnb-4bit},
  author={theprint},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/theprint/RRT1-3B}
}

Downloads last month: 5

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for theprint/RRT1-3B

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

unsloth/Qwen2.5-3B-Instruct-bnb-4bit

Adapter

(19)

this model

theprint
/

RRT1-3B