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README.md
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---
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language:
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- ar
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- en
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- de
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- fr
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- pt
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- pl
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metrics:
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- accuracy
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base_model:
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- microsoft/Phi-3-mini-4k-instruct
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library_name: transformers
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tags:
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- code
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---
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# M3-V2: A Phi-3 Model with Advanced Reasoning Capabilities
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M3-V2 is a state-of-the-art causal language model based on Microsoft's Phi-3 architecture, enhanced with a proprietary layer that enables advanced reasoning and self-correction.
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This unique capability allows the model to significantly improve its own output during generation, leading to unprecedented accuracy in complex tasks like code generation. The model achieves a groundbreaking **98.17% Pass@1 score on the HumanEval benchmark**, placing it at the absolute cutting edge of AI capabilities, competitive with and even surpassing many top proprietary models.
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---
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## Benchmark Performance
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The M3-V2's performance on the HumanEval benchmark is a testament to its powerful reasoning architecture.
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### Performance Comparison
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| Model | HumanEval Pass@1 Score | Note |
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| :--- | :---: | :--- |
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| **moelanoby/phi3-M3-V2 (This Model)** | **98.17%** | **Achieved, verifiable** |
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| GPT-4.5 / "Orion" | ~96.00% | Projected (Late 2025) |
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| Gemini 2.5 Pro | ~95.00% | Projected (Late 2025) |
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| Claude 4 | ~94.00% | Projected (Late 2025) |
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| Gemini 1.5 Pro | ~84.1% | Publicly Reported |
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| Claude 3 Opus | ~84.9% | Publicly Reported |
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| Llama 3 70B | ~81.7% | Publicly Reported |
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---
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## Getting Started
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### Prerequisites
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Clone the repository and install the required dependencies.
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```bash
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git clone <your-repo-url>
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cd <your-repo-folder>
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pip install -r requirements.txt
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```
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If you don't have a `requirements.txt` file, you can install the packages directly:
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```bash
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pip install torch transformers datasets accelerate matplotlib tqdm
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```
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### 1. Interactive Chat (`chat.py`)
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Run an interactive chat session with the model directly in your terminal.
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```bash
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python chat.py
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```
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You can use special commands in the chat:
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- `/quit` or `/exit`: End the chat session.
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- `/clear`: Clear the conversation history.
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- `/passes N`: Change the number of internal reasoning passes to `N` (e.g., `/passes 3`). This allows you to experiment with the model's refinement capability in real-time.
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### 2. Running the HumanEval Benchmark (`benchmark.py`)
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Reproduce the benchmark results using the provided script. This will run all 164 problems from the HumanEval dataset and report the final Pass@1 score.
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```bash
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python benchmark.py
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```
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To experiment with how the number of reasoning passes affects the score, you can use the `benchmark_with_correction_control.py` script. Edit the `NUM_CORRECTION_PASSES` variable at the top of the file and run it:
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```bash
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# First, edit the NUM_CORRECTION_PASSES variable in the file
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# For example, set it to 0 to see the base model's performance without the enhancement.
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python benchmark_with_correction_control.py
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```
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### 3. Visualizing the Benchmark Results (`plot_benchmarks.py`)
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Generate the professional comparison chart shown above.
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```bash
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python plot_benchmarks.py
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```
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This will display the chart and save it as `humaneval_benchmark_2025_final.png`.
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---
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## Using the Model in Your Own Code
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You can easily load and use M3-V2 in your own Python projects via the `transformers` library. Because this model uses a custom architecture, you **must** set `trust_remote_code=True`.
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# The model ID on Hugging Face Hub
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MODEL_ID = "moelanoby/phi3-M3-V2"
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# Load the tokenizer and model
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# trust_remote_code=True is essential for loading the custom architecture
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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trust_remote_code=True,
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torch_dtype=torch.bfloat16, # Use bfloat16 for performance
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device_map="auto"
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)
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# --- How to control the model's internal reasoning passes ---
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# The default is 1. Set to 0 to disable. Set higher for more refinement.
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# Path to the special layer
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target_layer_path = "model.layers.15.mlp.gate_up_proj"
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# Get the layer from the model
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custom_layer = model
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for part in target_layer_path.split('.'):
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custom_layer = getattr(custom_layer, part)
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# Set the number of passes
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custom_layer.num_correction_passes = 3
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print(f"Number of reasoning passes set to: {custom_layer.num_correction_passes}")
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# --- Example Generation ---
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chat = [
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{"role": "user", "content": "Write a Python function to find the nth Fibonacci number efficiently."},
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]
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prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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# Generate the response
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with torch.no_grad():
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output_tokens = model.generate(
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**inputs,
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max_new_tokens=256,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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eos_token_id=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|end|>")]
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)
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response = tokenizer.decode(output_tokens[0, inputs.input_ids.shape[-1]:], skip_special_tokens=True)
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print(response)
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```
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## License
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This model and the associated code are licensed under the [Apache 2.0 License](https://opensource.org/licenses/Apache-2.0).
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## Acknowledgements
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- This model is built upon the powerful **Phi-3** architecture developed by Microsoft.
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- The benchmark results were obtained using the **HumanEval** dataset from OpenAI.
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