初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Nu2-Lupi-Qwen-14B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+pipeline_tag: text-generation
+datasets:
+- madrylab/gsm8k-platinum
+tags:
+- sft
+- text-generation-inference
+- math
+- vLLM
+- trl
+library_name: transformers
+language:
+- en
+base_model:
+- prithivMLmods/Porpoise-Opus-14B-Exp
+---
+
+![zsdxvdsfvzds.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/pMkSKkDe-VPmXA00ZkGdc.png)
+
+# **Nu2-Lupi-Qwen-14B**  
+
+Nu2-Lupi-Qwen-14B is based on the Qwen 2.5 14B modality architecture, designed to enhance mathematical reasoning capabilities. This model is optimized for complex problem-solving, logical deduction, and multi-step mathematical reasoning. It has been fine-tuned using the ***gsm8k-platinum*** dataset to improve accuracy, structured responses, and contextual understanding in mathematical domains.  
+
+## **Key Improvements**  
+1. **Enhanced Mathematical Proficiency**: The model excels in solving complex mathematical problems, including algebra, calculus, and number theory.  
+2. **Advanced Reasoning Capabilities**: Optimized for step-by-step problem-solving, enabling clear and logical explanations for mathematical queries.  
+3. **Improved Instruction Following**: Capable of understanding and executing multi-step instructions with precision, ensuring structured and coherent outputs.  
+4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed problem breakdowns.  
+5. **Multilingual Mathematical Reasoning**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.  
+
+## **Quickstart with transformers**  
+
+Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Nu2-Lupi-Qwen-14B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve the equation: 3x + 5 = 14."
+messages = [
+    {"role": "system", "content": "You are a mathematical reasoning assistant."},
+    {"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]
+```
+
+## **Intended Use**  
+1. **Mathematical Reasoning and Problem-Solving**:  
+   Fine-tuned for high-precision mathematical problem-solving, including algebra, geometry, calculus, and logic puzzles.  
+
+2. **Educational and Academic Assistance**:  
+   Ideal for students, educators, and researchers looking for structured explanations and step-by-step solutions.  
+
+3. **Conversational AI with Mathematical Focus**:  
+   Supports intelligent chatbot applications that require mathematical comprehension and dynamic response generation.  
+
+4. **Data Science and Analytical Processing**:  
+   Capable of analyzing mathematical datasets, generating structured numerical insights, and assisting with automation.  
+
+5. **Long-Form Mathematical Content Generation**:  
+   Can generate detailed problem breakdowns, mathematical reports, and research-based content with high coherence.  
+
+## **Limitations**  
+1. **Hardware Requirements**:  
+   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  
+
+2. **Potential Bias in Responses**:  
+   While fine-tuned for accuracy, outputs may still reflect biases present in training data.  
+
+3. **Inconsistent Creative Outputs**:  
+   May generate varying results when handling abstract or theoretical mathematical concepts.  
+
+4. **Limited Real-World Awareness**:  
+   Does not have access to real-time mathematical discoveries beyond its training cutoff.  
+
+5. **Error Propagation in Extended Outputs**:  
+   Minor calculation errors in early steps may affect overall problem solutions in long-form responses.  
+
+6. **Prompt Sensitivity**:   
+   The effectiveness of responses may depend on how well the mathematical problem is structured within the input prompt.