初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Logics-Qwen3-Math-4B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+tags:
+- trl
+- math
+- text-generation-inference
+- code
+datasets:
+- nvidia/OpenCodeReasoning
+- nvidia/OpenMathReasoning
+- prithivMLmods/Helios-R-6M
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B-Thinking-2507
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/IObi572uIr3vg89VuZD5x.png)
+
+# **Logics-Qwen3-Math-4B**
+
+> **Logics-Qwen3-Math-4B** is a reasoning-focused model fine-tuned on **Qwen3-4B-Thinking-2507** for **mathematical reasoning** and **logical coding**, trained on **OpenMathReasoning**, **OpenCodeReasoning**, and **Helios-R-6M** datasets. It excels in structured **mathematical problem solving**, **algorithmic logic**, and **probabilistic reasoning**, making it ideal for educators, researchers, and developers focused on computational logic and math.
+
+## **Key Features**
+
+1. **Mathematical & Logical Reasoning**
+   Fine-tuned for high-precision math reasoning, algorithmic problem-solving, and logical coding tasks.
+
+2. **Event-Driven & Probabilistic Modeling**
+   Performs probability-based simulations, structured decision-making, and multi-step logical reasoning with strong accuracy.
+
+3. **Multilingual Problem Solving**
+   Supports math and logic tasks across multiple languages, suitable for global research and education workflows.
+
+4. **Hybrid Symbolic-Algorithmic Thinking**
+   Combines structured logic, symbolic computation, and probabilistic inference to handle uncertainty-driven problems efficiently.
+
+5. **Structured Output Mastery**
+   Generates outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, enabling smooth integration into technical and research workflows.
+
+6. **Optimized 4B Parameter Footprint**
+   Deployable on **mid-range GPUs**, **offline clusters**, and **edge devices**, maintaining high reasoning quality while being resource-efficient.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Logics-Qwen3-Math-4B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve the equation x^2 - 5x + 6 = 0 and show all reasoning steps."
+
+messages = [
+    {"role": "system", "content": "You are a math and logic tutor skilled in algebra, probability, and structured programming reasoning."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+## **Intended Use**
+
+* High-precision mathematical reasoning and problem-solving
+* Algorithmic logic, structured coding tasks, and probability analysis
+* Educational and research-focused workflows
+* Deployment on mid-resource environments with efficient reasoning
+* Structured data and technical content generation
+
+## **Limitations**
+
+* Focused on math and logic—less suited for creative writing or casual conversation
+* Very complex multi-hop reasoning may challenge the 4B parameter capacity
+* Prioritizes structured reasoning over conversational tone
+* Outputs may be inconsistent for extremely long or cross-domain multi-document contexts