94 lines
3.4 KiB
Markdown
94 lines
3.4 KiB
Markdown
---
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license: mit
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base_model:
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- microsoft/Phi-4-mini-reasoning
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- trl
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- text-generation-inference
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- math
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- code
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---
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# **TOI-157-Phi-4-Reasoning-Mini**
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> **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.
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> 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.
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## **Key Features**
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1. **Abliterated Reasoning**
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Enhanced reasoning precision through polished token probability distributions in Phi-based models, ensuring balanced and context-aware outputs.
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2. **Event Simulation & Logical Analysis**
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Models random events, probability-driven reasoning, and logical decision-making with strong consistency.
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3. **Multilingual Mathematical & General-Purpose Problem Solving**
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Delivers robust performance in **math**, **probability**, and **structured multilingual tasks**, enabling wide applicability in global research and education.
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4. **Hybrid Symbolic-Probabilistic Thinking**
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Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks.
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5. **Structured Output Mastery**
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Generates well-structured outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, supporting technical workflows and data-driven research.
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6. **Optimized Lightweight Footprint**
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Compact **mini parameter size**, deployable on **edge devices**, **offline clusters**, and **mid-range GPUs**, while maintaining reasoning quality.
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## **Quickstart with Transformers**
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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torch.random.manual_seed(0)
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model_id = "prithivMLmods/TOI-157-Phi-4-Reasoning-Mini"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="cuda",
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torch_dtype="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [{
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"role": "user",
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"content": "How to solve 3*x^2 + 4*x + 5 = 1?"
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}]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_dict=True,
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return_tensors="pt",
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)
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outputs = model.generate(
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**inputs.to(model.device),
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max_new_tokens=32768,
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temperature=0.8,
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top_p=0.95,
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do_sample=True,
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)
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outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1]:])
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print(outputs[0])
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```
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## **Intended Use**
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* Balanced multilingual reasoning and probability modeling
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* Event simulation, uncertainty analysis, and structured problem solving
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* Educational and research-focused reasoning tasks
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* Lightweight deployment in constrained environments
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* Technical content and structured data generation
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## **Limitations**
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* Focused on reasoning and mathematics—less suited for creative writing
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* Smaller size may limit depth on highly complex, multi-step tasks
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* Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone. |