TOI-157-Phi-4-Reasoning-Mini/README.md

license, base_model, language, pipeline_tag, library_name, tags

license	base_model	language	pipeline_tag	library_name	tags
mit	microsoft/Phi-4-mini-reasoning	en	text-generation	transformers	trl text-generation-inference math code

TOI-157-Phi-4-Reasoning-Mini

TOI-157-Phi-4-Reasoning-Mini is a reasoning-focused model fine-tuned on Microsoft’s Phi-4-mini-reasoning for Edge-level Abliterated Reasoning and optimized polished token probabilities, enhancing balanced multilingual generation across mathematics and general-purpose reasoning. It specializes in event-driven logic, structured analysis, and precise probabilistic modeling—making it an ideal tool for researchers, educators, and developers working with uncertainty and structured reasoning.

Key Features

1. Abliterated Reasoning Enhanced reasoning precision through polished token probability distributions in Phi-based models, ensuring balanced and context-aware outputs.

2. Event Simulation & Logical Analysis Models random events, probability-driven reasoning, and logical decision-making with strong consistency.

3. Multilingual Mathematical & General-Purpose Problem Solving Delivers robust performance in math, probability, and structured multilingual tasks, enabling wide applicability in global research and education.

4. Hybrid Symbolic-Probabilistic Thinking Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks.

5. Structured Output Mastery Generates well-structured outputs in LaTeX, Markdown, JSON, CSV, and YAML, supporting technical workflows and data-driven research.

6. Optimized Lightweight Footprint Compact mini parameter size, deployable on edge devices, offline clusters, and mid-range GPUs, while maintaining reasoning quality.

Quickstart with Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)

model_id = "prithivMLmods/TOI-157-Phi-4-Reasoning-Mini"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cuda",
    torch_dtype="auto",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [{
    "role": "user",
    "content": "How to solve 3*x^2 + 4*x + 5 = 1?"
}]   
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)

outputs = model.generate(
    **inputs.to(model.device),
    max_new_tokens=32768,
    temperature=0.8,
    top_p=0.95,
    do_sample=True,
)
outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])

print(outputs[0])

Intended Use

• Balanced multilingual reasoning and probability modeling
• Event simulation, uncertainty analysis, and structured problem solving
• Educational and research-focused reasoning tasks
• Lightweight deployment in constrained environments
• Technical content and structured data generation

Limitations

• Focused on reasoning and mathematics—less suited for creative writing
• Smaller size may limit depth on highly complex, multi-step tasks
• Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone.