Update README.md

README.md

@@ -24,4 +24,81 @@ tags:
 - Java
 - Qwen
 - Math
----
+---
+# **Raptor-X2**
+
+> Raptor-X2 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for advanced mathematical explanations, scientific reasoning, and general-purpose coding. It excels in contextual understanding, logical deduction, and multi-step problem-solving. Raptor-X2 has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence.
+
+Key improvements include:  
+1. **Enhanced Mathematical Reasoning**: Provides step-by-step explanations for complex mathematical problems, making it useful for students, researchers, and professionals.
+2. **Advanced Scientific Understanding**: Excels in explaining scientific concepts across physics, chemistry, biology, and engineering.
+3. **General-Purpose Coding**: Capable of generating, debugging, and optimizing code across multiple programming languages, supporting software development and automation.
+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 responses.
+5. **Multilingual Proficiency**: 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/Raptor-X2"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the fundamental theorem of calculus."
+messages = [
+    {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
+    {"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 Explanation**:  
+   Designed for providing step-by-step solutions to mathematical problems, including algebra, calculus, and discrete mathematics.  
+2. **Scientific Reasoning**:  
+   Suitable for explaining scientific theories, conducting physics simulations, and solving chemistry equations.  
+3. **Programming and Software Development**:  
+   Capable of generating, analyzing, and optimizing code in multiple programming languages.  
+4. **Educational Assistance**:  
+   Helps students and researchers by providing explanations, summaries, and structured learning material.  
+5. **Multilingual Applications**:  
+   Supports global communication, translations, and multilingual content generation.  
+6. **Long-Form Content Generation**:  
+   Can generate extended responses, including research papers, documentation, and technical reports.  
+
+# **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 designed to be neutral, outputs may still reflect biases present in training data.  
+3. **Complexity in Some Scientific Domains**:  
+   While proficient in general science, highly specialized fields may require verification.  
+4. **Limited Real-World Awareness**:  
+   Does not have access to real-time events beyond its training cutoff.  
+5. **Error Propagation in Extended Outputs**:  
+   Minor errors in early responses may affect overall coherence in long-form outputs.  
+6. **Prompt Sensitivity**:  
+   The effectiveness of responses may depend on how well the input prompt is structured.