---

base_model: microsoft/Phi-4-reasoning-plus

---
This is a quantization of the [Phi-4-reasoning-plus](https://huggingface.co/microsoft/Phi-4-reasoning-plus).

Phi-4-reasoning-plus, developed by Microsoft Research, stands out as a state-of-the-art language model specialized in reasoning and logic, particularly excelling in domains like math, science, and coding. Finetuned from the Phi-4 model, it uniquely combines supervised learning with reinforcement learning, enhancing accuracy and offering advanced reasoning capabilities in memory-constrained and latency-sensitive environments. The model generates responses with a distinct two-section format: a detailed reasoning chain-of-thought process followed by a concise solution, ensuring thorough and accurate answers. Despite being relatively compact with 14 billion parameters, it delivers strong performance across a wide range of complex reasoning tasks and demonstrates the capacity to maintain coherence over extended inputs, making it particularly suited for deep, multi-step reasoning applications.
## Evaluations
This model provides an accuracy recovery of 100.0%. 

| __English__   |   __[Phi-4-reasoning-plus](https://huggingface.co/microsoft/Phi-4-reasoning-plus)__ |   __[Phi-4-reasoning-plus-FP8-Dynamic (this)](https://huggingface.co/cortecs/Phi-4-reasoning-plus-FP8-Dynamic)__ |
|:--------------|------------------------------------------------------------------------------------:|-----------------------------------------------------------------------------------------------------------------:|
| Avg.          |                                                                               70.77 |                                                                                                            70.77 |
| ARC           |                                                                               65.7  |                                                                                                            65.5  |
| Hellaswag     |                                                                               69    |                                                                                                            69.5  |
| MMLU          |                                                                               77.61 |                                                                                                            77.3  |

We did not check for data contamination.
     Evaluation was done using [Eval. Harness](https://github.com/EleutherAI/lm-evaluation-harness) with `limit=1000`. 
    
## Usage
Install **vLLM** and 
    run the [server](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#openai-compatible-server):
    
```
python -m vllm.entrypoints.openai.api_server --model cortecs/Phi-4-reasoning-plus-FP8-Dynamic --max-model-len 32768 --gpu-memory-utilization 0.95
```
Access the model:
```
curl http://localhost:8000/v1/completions     -H "Content-Type: application/json"     -d ' {
        "model": "cortecs/Phi-4-reasoning-plus-FP8-Dynamic",
        "prompt": "San Francisco is a"
    } '
```