Update README.md

README.md

@@ -12,4 +12,99 @@ tags:
 - qwen2.5
 - cot
 - lcot
----
+---
+
+# **Taurus-Opus-7B-Elite**
+
+Taurus-Opus-7B-Elite is based on a 7B-parameter architecture inspired by Qwen 2.5, optimized to deliver exceptional reasoning, contextual understanding, and problem-solving capabilities. It has been fine-tuned with a focus on chain-of-thought (CoT) reasoning using a specialized dataset for tasks requiring logical deductions and multi-step problem-solving. Despite its reduced parameter count, Taurus-Opus-7B-Elite remains highly efficient and versatile, tailored for a range of applications such as instruction-following, structured data processing, and multilingual tasks.
+
+# **Key Improvements**
+
+1. **Compact Yet Powerful**:  
+   Despite being a 7B-parameter model, Taurus-Opus demonstrates powerful reasoning and understanding capabilities comparable to larger models due to advanced optimization techniques.
+
+2. **Enhanced Efficiency**:  
+   Optimized for faster inference and reduced computational costs, making it suitable for deployments on devices with limited resources.
+
+3. **Instruction Following**:  
+   Improved capabilities in understanding and executing complex instructions while generating long texts (up to 4K tokens).
+
+4. **Structured Data Processing**:  
+   Excels at analyzing tables, JSON, and other structured data formats, ensuring accurate and structured outputs.
+
+5. **Multilingual Proficiency**:  
+   Supports 20+ languages, maintaining accuracy and fluency in common languages such as English, Chinese, Spanish, and French.
+
+6. **Streamlined Long-Context Support**:  
+   Supports up to 64K tokens, providing robust contextual understanding for long-chain reasoning tasks.
+
+# **Quickstart with transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Taurus-Opus-7B-Elite"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain why reasoning is critical in solving complex problems."
+messages = [
+    {"role": "system", "content": "You are Taurus, an advanced AI assistant optimized for reasoning and problem-solving."},
+    {"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=256
+)
+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. **Reasoning and Contextual Understanding**:  
+   Tailored for tasks that require logical deductions and contextual analysis, suitable for educational and professional use cases.
+
+2. **Mathematical Reasoning**:  
+   Adept at solving mathematical problems and calculations, making it ideal for STEM applications.
+
+3. **Code Assistance**:  
+   Provides support for generating, debugging, and optimizing code in a variety of programming languages.
+
+4. **Multilingual Tasks**:  
+   Enables global applications, including multilingual content generation, translation, and conversational AI.
+
+5. **Content Generation**:  
+   Generates high-quality long-form text for reports, articles, and other professional documents.
+
+# **Limitations**
+
+1. **Reduced Parameter Count**:  
+   While efficient, it may not achieve the same depth of understanding as larger models like 14B-parameter counterparts in some complex tasks.
+
+2. **Hardware Requirements**:  
+   Though lighter than larger models, it still requires a GPU or high-performance CPU for optimal performance.
+
+3. **Multilingual Accuracy**:  
+   Performance may vary for less-resourced languages, with minor inaccuracies in nuanced translations.
+
+4. **Error Propagation in Long Outputs**:  
+   Similar to larger models, early output errors in long-text generation can affect the coherence of the final text.
+
+5. **Prompt Sensitivity**:  
+   Requires well-structured prompts for best performance, necessitating some user familiarity with prompt design.