初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/TOI-157-Phi-4-Reasoning-Mini
Source: Original Platform

README.md

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+---
+license: mit
+base_model:
+- microsoft/Phi-4-mini-reasoning
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- trl
+- text-generation-inference
+- math
+- code
+---
+
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jhaXgo4VgX-9HbP3wtu7s.png)
+
+# **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**
+
+```python
+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.