Update README.md

README.md

@@ -11,4 +11,68 @@ tags:
 - Qwen
 - 5M-Logits
 - trl
----
+---
+# **Megatron-Corpus-14B-Exp**  
+
+Megatron-Corpus-14B-Exp is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. It has been fine-tuned on a synthetic dataset based on math corpus, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation.  
+
+### **Key Improvements**  
+1. **Advanced Reasoning & Logic**: Optimized for multi-step problem-solving, logical deduction, and contextual analysis.  
+2. **Fine-Tuned Instruction Following**: Generates precise responses, structured outputs (e.g., JSON), and extended long-form text (8K+ tokens).  
+3. **Greater Adaptability**: Excels in role-playing, multi-turn dialogues, and diverse system prompts.  
+4. **Long-Context Support**: Handles up to **128K tokens** and generates up to **8K tokens** per output.  
+5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more.  
+
+### **Quickstart with Transformers**  
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Megatron-Corpus-14B-Exp"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain the concept of logical reasoning in AI."
+messages = [
+    {"role": "system", "content": "You are an expert AI assistant specialized in reasoning and logic."},
+    {"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**  
+- **Advanced Logical & Analytical Reasoning**: Designed for problem-solving, multi-step deductions, and cognitive reasoning tasks.  
+- **Mathematical & Scientific Computation**: Supports theorem proving, complex calculations, and scientific knowledge retrieval.  
+- **Code Generation & Debugging**: Generates optimized code, detects errors, and improves programming workflows.  
+- **Structured Data Analysis**: Processes tables, JSON, and structured formats for data-centric applications.  
+- **Multilingual Reasoning & Translation**: High proficiency across **29+ languages** for international applications.  
+- **Extended Text Generation**: Capable of generating research papers, instructional guides, and in-depth reports.  
+
+### **Limitations**  
+1. **High Computational Requirements**: Due to its **14B parameters** and **128K context support**, it requires powerful GPUs or TPUs for efficient inference.  
+2. **Language-Specific Variability**: Performance may differ across supported languages, especially for low-resource languages.  
+3. **Potential Error Accumulation**: Long-form text generation can introduce inconsistencies over extended outputs.  
+4. **Limited Real-World Awareness**: Knowledge is restricted to training data and may not reflect recent world events.  
+5. **Prompt Sensitivity**: The quality of responses depends on the specificity and clarity of the input prompt.