--- 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**): - [Phi4.Turn.R1Distill-Lora1](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill-Lora1) - [Phi4.Turn.R1Distill-Lora2](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill-Lora2) - [Phi4.Turn.R1Distill-Lora3](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill-Lora3) - [Phi4.Turn.R1Distill-Lora4](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill-Lora4) - [Phi4.Turn.R1Distill-Lora5](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill-Lora5) - [Phi4.Turn.R1Distill-Lora6](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill-Lora6) - [Phi4.Turn.R1Distill-Lora7](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill-Lora7) - [Phi4.Turn.R1Distill-Lora8](https://huggingface.co/Quazim0t0/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** - [Phi4.Turn.R1Distill.Q8_0](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill.Q8_0) - [Phi4.Turn.R1Distill.Q4_k](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill.Q4_k) - [Phi4.Turn.R1Distill.16bit](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill.16bit) ### **Version 1.1** - [Phi4.Turn.R1Distill_v1.1_Q4_k](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill_v1.1_Q4_k) ### **Version 1.2** - [Phi4.Turn.R1Distill_v1.2_Q4_k](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill_v1.2_Q4_k) ### **Version 1.3** - [Phi4.Turn.R1Distill_v1.3_Q4_k-GGUF](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill_v1.3_Q4_k-GGUF) ### **Version 1.4** - [Phi4.Turn.R1Distill_v1.4_Q4_k-GGUF](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill_v1.4_Q4_k-GGUF) ### **Version 1.5** - [Phi4.Turn.R1Distill_v1.5_Q4_k-GGUF](https://huggingface.co/Quazim0t0/Phi4.Turn.R1Distill_v1.5_Q4_k-GGUF) --- ## Usage ### **Loading LoRA Adapters with `transformers` and `peft`** To load and apply the LoRA adapters on Phi-4, use the following approach: ```python 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|