Uploaded model
- Developed by: Quazim0t0
- Finetuned from model : unsloth/phi-4-unsloth-bnb-4bit
- GGUF
- Trained for 8 Hours on A800 with the Bespoke Stratos 17k Dataset.
- Trained for 6 Hours on A800 with the Bespoke Stratos 35k Dataset.
- Trained for 2 Hours on A800 with the Benford's Law Reasoning Small 430 Row Dataset, ensuring no overfitting.
- Trained for 4 Hours on A800 with the Sky-T1_data_17k Dataset
- Trained for 2 Hours on A800 with the Openthoughts 114k Dataset.
- 15$ 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):
- Phi4.Turn.R1Distill-Lora1
- Phi4.Turn.R1Distill-Lora2
- Phi4.Turn.R1Distill-Lora3
- Phi4.Turn.R1Distill-Lora4
- Phi4.Turn.R1Distill-Lora5
- Phi4.Turn.R1Distill-Lora6
- Phi4.Turn.R1Distill-Lora7
- Phi4.Turn.R1Distill-Lora8
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()
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