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Phi4.Turn.R1Distill.Q8_0

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metadata
base_model: unsloth/phi-4-unsloth-bnb-4bit
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - gguf
license: apache-2.0
language:
  - en
datasets:
  - bespokelabs/Bespoke-Stratos-17k

Uploaded model

  • Developed by: Quazim0t0
  • License: apache-2.0
  • Finetuned from model : unsloth/phi-4-unsloth-bnb-4bit
  • GGUF
  • Trained for 8 Hours on A800 with the Bespoke Stratos 17k Dataset.
  • 10$ Training...I'm actually amazed by the results.

If using this model for Open WebUI here is a simple function to organize the models responses: https://openwebui.com/f/quaz93/phi4_turn_r1_distill_thought_function_v1

Phi4 Turn R1Distill LoRA Adapters

Overview

These LoRA adapters were trained using diverse reasoning datasets that incorporate structured Thought and Solution responses to enhance logical inference. This project was designed to test the R1 dataset on Phi-4, aiming to create a lightweight, fast, and efficient reasoning model.

All adapters were fine-tuned using an NVIDIA A800 GPU, ensuring high performance and compatibility for continued training, merging, or direct deployment.
As part of an open-source initiative, all resources are made publicly available for unrestricted research and development.

LoRA Adapters

Below are the currently available LoRA fine-tuned adapters (as of January 30, 2025):

GGUF Full & Quantized Models

To facilitate broader testing and real-world inference, GGUF Full and Quantized versions have been provided for evaluation on Open WebUI and other LLM interfaces.

Version 1

Version 1.1

Version 1.2

Version 1.3

Version 1.4

Version 1.5

Usage

Loading LoRA Adapters with transformers and peft

To load and apply the LoRA adapters on Phi-4, use the following approach:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "microsoft/Phi-4"
lora_adapter = "Quazim0t0/Phi4.Turn.R1Distill-Lora1"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, lora_adapter)

model.eval()