Phi 4 Reasoning Plus Unsloth Bnb 4bit

🚀 Phi-4-reasoning-plus

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• 🌟 Fine-tune Phi-4 (14B) for free using our Google Colab notebook here!
• 📖 Read our Blog about Phi-4 support with our bug fixes: unsloth.ai/blog/phi4
• 📚 View the rest of our notebooks in our docs here.
• 💪 Run & export your fine-tuned model to Ollama, llama.cpp or HF.

🚀 Quick Start

Inference Parameters

Inference is better with temperature=0.8, top_p=0.95, and do_sample=True. For more complex queries, set the maximum number of tokens to 32k to allow for longer chain-of-thought (CoT).

Phi-4-reasoning-plus has shown strong performance on reasoning-intensive tasks. In our experiments, we extended its maximum number of tokens to 64k, and it handled longer sequences with promising results, maintaining coherence and logical consistency over extended inputs. This makes it a compelling option to explore for tasks that require deep, multi-step reasoning or extensive context.

Input Formats

Given the nature of the training data, always use ChatML template with the following system prompt for inference:

<|im_start|>system<|im_sep|>
Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} <\think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>
<|im_start|>user<|im_sep|>
What is the derivative of x^2?<|im_end|>
<|im_start|>assistant<|im_sep|>

With transformers

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-4-reasoning-plus")
model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-4-reasoning-plus", device_map="auto", torch_dtype="auto")

messages = [
    {"role": "system", "content": "You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:"},
    {"role": "user", "content": "What is the derivative of x^2?"},
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    inputs.to(model.device),
    max_new_tokens=4096,
    temperature=0.8,
    top_p=0.95,
    do_sample=True,
)
print(tokenizer.decode(outputs[0]))

With vllm

vllm serve microsoft/Phi-4-reasoning-plus --enable-reasoning --reasoning-parser deepseek_r1

Phi-4-reasoning-plus is also supported out-of-the-box by Ollama, llama.cpp, and any Phi-4 compatible framework.

✨ Features

• High-performance Reasoning: Phi-4-reasoning-plus is a state-of-the-art open-weight reasoning model, showing excellent performance in various reasoning tasks.
• Versatile Applications: Suitable for general-purpose AI systems and applications, especially in English, with strong capabilities in memory/compute constrained environments, latency bound scenarios, and reasoning and logic.
• Safety Assurance: Adopted a robust safety post-training approach via supervised fine-tuning (SFT) to ensure the model's safety and reliability.

📚 Documentation

Model Summary

Intended Use

Data Overview

Training Datasets

Our training data is a mixture of Q&A, chat format data in math, science, and coding. The chat prompts are sourced from filtered high-quality web data and optionally rewritten and processed through a synthetic data generation pipeline. We further include data to improve truthfulness and safety.

Benchmark Datasets

We evaluated Phi-4-reasoning-plus using the open-source Eureka evaluation suite and our own internal benchmarks to understand the model's capabilities. More specifically, we evaluate our model on:

Reasoning tasks:

• AIME 2025, 2024, 2023, and 2022: Math olympiad questions.
• GPQA - Diamond: Complex, graduate-level science questions.
• OmniMath: Collection of over 4000 olympiad-level math problems with human annotation.
• LiveCodeBench: Code generation benchmark gathered from competitive coding contests.
• 3SAT (3 - literal Satisfiability Problem) and TSP (Traveling Salesman Problem): Algorithmic problem solving.
• BA Calendar: Planning.
• Maze and SpatialMap: Spatial understanding.

General - purpose benchmarks:

• Kitab: Information retrieval.
• IFEval and ArenaHard: Instruction following.
• PhiBench: Internal benchmark.
• FlenQA: Impact of prompt length on model performance.
• HumanEvalPlus: Functional code generation.
• MMLU - Pro: Popular aggregated dataset for multitask language understanding.

Safety

Approach

Phi-4-reasoning-plus has adopted a robust safety post-training approach via supervised fine-tuning (SFT). This approach leverages a variety of both open-source and in-house generated synthetic prompts, with LLM-generated responses that adhere to rigorous Microsoft safety guidelines, e.g., User Understanding and Clarity, Security and Ethical Guidelines, Limitations, Disclaimers and Knowledge Scope, Handling Complex and Sensitive Topics, Safety and Respectful Engagement, Confidentiality of Guidelines and Confidentiality of Chain-of-Thoughts.

Safety Evaluation and Red - Teaming

Prior to release, Phi-4-reasoning-plus followed a multi-faceted evaluation approach. Quantitative evaluation was conducted with multiple open-source safety benchmarks and in-house tools utilizing adversarial conversation simulation. For qualitative safety evaluation, we collaborated with the independent AI Red Team (AIRT) at Microsoft to assess safety risks posed by Phi-4-reasoning-plus in both average and adversarial user scenarios. In the average user scenario, AIRT emulated typical single-turn and multi-turn interactions to identify potentially risky behaviors. The adversarial user scenario tested a wide range of techniques aimed at intentionally subverting the model's safety training including grounded-ness, jailbreaks, harmful content like hate and unfairness, violence, sexual content, or self-harm, and copyright violations for protected material. We further evaluate models on Toxigen, a benchmark designed to measure bias and toxicity targeted towards minority groups.

Please refer to the technical report for more details on safety alignment.

Model Quality

At the high - level overview of the model quality on representative benchmarks. For the tables below, higher numbers indicate better performance:

Overall, Phi-4-reasoning and Phi-4-reasoning-plus, with only 14B parameters, performs well across a wide range of reasoning tasks, outperforming significantly larger open-weight models such as DeepSeek-R1 distilled 70B model and approaching the performance levels of full DeepSeek R1 model. We also test the models on multiple new reasoning benchmarks for algorithmic problem solving and planning, including 3SAT, TSP, and BA - Calendar. These new tasks are nominally out-of-domain for the models as the training process did not intentionally target these skills, but the models still show strong generalization to these tasks. Furthermore, when evaluating performance against standard general abilities benchmarks such as instruction following or non-reasoning tasks, we find that our new models improve significantly from Phi-4, despite the post-training being focused on reasoning skills in specific domains.

Responsible AI Considerations

Like other language models, Phi-4-reasoning-plus can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:

• Quality of Service: The model is trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance.

📄 License

The model is released under the MIT license. You can find the license details here.