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theprint/RRT1-3B
RRT1-3B is a text generation model from theprint. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as apache-2.0.
A fine-tuned 3B parameter model specialized for reasoning and chain-of-thought tasks
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From the Hugging Face model README
A fine-tuned 3B parameter model specialized for reasoning and chain-of-thought tasks
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.
Reasoning, chain-of-thought, and general instruction following
ShareGPT conversations with chain-of-thought reasoning examples
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)
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)May hallucinate or provide incorrect information. Not suitable for critical decision making.
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}
}