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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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+
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+
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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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+
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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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+ ---
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+
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+ ## Benchmark Performance
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+
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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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+
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+ ![HumanEval Benchmark Chart](humaneval_benchmark_2025_final.png)
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+
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+ ### Performance Comparison
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+
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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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+ ---
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+
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+ ## Getting Started
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+
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+ ### Prerequisites
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+
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+ Clone the repository and install the required dependencies.
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+
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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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+
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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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+
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+ ### 1. Interactive Chat (`chat.py`)
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+
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+ Run an interactive chat session with the model directly in your terminal.
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+
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+ ```bash
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+ python chat.py
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+ ```
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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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+
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+ ### 2. Running the HumanEval Benchmark (`benchmark.py`)
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+
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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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+
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+ ```bash
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+ python benchmark.py
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+ ```
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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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+
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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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+
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+ ### 3. Visualizing the Benchmark Results (`plot_benchmarks.py`)
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+
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+ Generate the professional comparison chart shown above.
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+
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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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+ ---
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+
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+ ## Using the Model in Your Own Code
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+
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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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+
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+ ```python
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+ import torch
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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.