Update README.md

README.md

@@ -1,3 +1,103 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- trl
+- vlm
+- sft
+- code
+- math
+---
+![ccccccccccccc.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/Ii0oEprS2lm6Zoama7CPe.png)
+
+# **Gauss-Opus-14B-R999**  
+
+> Gauss-Opus-14B-R999 is based on the Qwen 2.5 14B modality architecture, designed to enhance mathematical and constructive reasoning capabilities. This model is optimized for advanced problem-solving, logical structuring, and mathematical comprehension. It excels in numerical reasoning, theorem proving, and multi-step calculations. Fine-tuned with specialized datasets in mathematics, physics, and formal logic, it delivers structured, high-accuracy outputs with a strong emphasis on precision and clarity.  
+
+## **Key Improvements**  
+1. **Enhanced Mathematical Reasoning**: Optimized for algebra, calculus, number theory, and logical deduction, providing precise and structured solutions.  
+2. **Improved Instruction Following**: Capable of interpreting and following complex mathematical proofs, equations, and problem-solving instructions with high accuracy.  
+3. **Versatile Adaptability**: Handles diverse reasoning tasks, including step-by-step solutions, mathematical proofs, and constructive problem-solving.  
+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 mathematical derivations.  
+5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more, ensuring broad accessibility.  
+
+## **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/Gauss-Opus-14B-R999"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve the integral \int x^2 dx and explain the steps."
+messages = [
+    {"role": "system", "content": "You are a mathematical assistant specialized in problem-solving and theorem proving."},
+    {"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 Problem-Solving**:  
+   Designed for high-precision mathematical reasoning, step-by-step calculations, and structured solutions.  
+
+2. **Theorem Proving and Logical Reasoning**:  
+   Useful for verifying mathematical proofs, formal logic derivations, and theorem-based reasoning.  
+
+3. **STEM Education and Research**:  
+   Ideal for educators, researchers, and students requiring assistance in complex problem-solving and mathematical modeling.  
+
+4. **Algorithm Development and Optimization**:  
+   Supports structured reasoning in algorithmic problem-solving, coding optimizations, and computational logic.  
+
+5. **Long-Form Explanatory Content**:  
+   Can generate detailed mathematical articles, research summaries, and explanatory guides with structured step-by-step reasoning.  
+
+6. **Multilingual Mathematical Assistance**:  
+   Supports global accessibility for mathematical discussions, translations, and problem explanations across multiple languages.  
+
+## **Limitations**  
+1. **Hardware Requirements**:  
+   Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.  
+
+2. **Potential Bias in Training Data**:  
+   While optimized for accuracy, the model may inherit biases from training data in certain problem-solving approaches.  
+
+3. **Complexity in Abstract Theories**:  
+   May struggle with highly abstract or unsolved mathematical problems that require intuitive leaps beyond computational logic.  
+
+4. **Error Propagation in Extended Proofs**:  
+   Small errors in early steps may compound in multi-step proofs and long-form mathematical derivations.  
+
+5. **Prompt Sensitivity**:  
+   The quality of responses depends on how well the problem is structured and framed within the input prompt.