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Phi4.Turn.R1Distill_v1.5.1_Q4_k-GGUF

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metadata
language:
  - en
license: apache-2.0
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - gguf
base_model: unsloth/phi-4-unsloth-bnb-4bit
datasets:
  - bespokelabs/Bespoke-Stratos-17k
  - bespokelabs/Bespoke-Stratos-35k
  - NovaSky-AI/Sky-T1_data_17k
  - Quazim0t0/BenfordsLawReasoningJSON
  - open-thoughts/OpenThoughts-114k
model-index:
  - name: Phi4.Turn.R1Distill_v1.5.1-Tensors
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 29.95
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Quazim0t0/Phi4.Turn.R1Distill_v1.5.1-Tensors
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 49.22
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Quazim0t0/Phi4.Turn.R1Distill_v1.5.1-Tensors
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 1.59
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Quazim0t0/Phi4.Turn.R1Distill_v1.5.1-Tensors
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 2.46
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Quazim0t0/Phi4.Turn.R1Distill_v1.5.1-Tensors
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 7.04
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Quazim0t0/Phi4.Turn.R1Distill_v1.5.1-Tensors
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 45.75
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Quazim0t0/Phi4.Turn.R1Distill_v1.5.1-Tensors
          name: Open LLM Leaderboard

TurnPhi Project

  • 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 6 Hours on A800 with the Openthoughts 114k Dataset.
  • 18$ Training...I'm actually amazed by the results.

OpenWeb UI Function

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()
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/Quazim0t0__Phi4.Turn.R1Distill_v1.5.1-Tensors-details)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |22.67|
|IFEval (0-Shot)    |29.95|
|BBH (3-Shot)       |49.22|
|MATH Lvl 5 (4-Shot)| 1.59|
|GPQA (0-shot)      | 2.46|
|MuSR (0-shot)      | 7.04|
|MMLU-PRO (5-shot)  |45.75|