Update README.md

README.md

@@ -16,34 +16,34 @@ tags:
 
 # **Taurus-Opus-7B**
 
-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.
+Taurus-Opus-7B is built upon the LLaMA (Large Language Model Meta AI) 7B architecture, optimized to provide advanced reasoning capabilities while maintaining efficiency. With 7 billion parameters, it strikes a balance between performance and computational resource requirements. The model has been fine-tuned with a focus on chain-of-thought (CoT) reasoning, leveraging specialized datasets to enhance its problem-solving abilities. Taurus-Opus-7B is designed for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and coding assistance.
 
-# **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.
+# **Key Features and Improvements**
 
-2. **Enhanced Efficiency**:  
-   Optimized for faster inference and reduced computational costs, making it suitable for deployments on devices with limited resources.
+1. **Optimized Reasoning Capabilities**:  
+   The model showcases significant improvements in context understanding, reasoning, and mathematical problem-solving through fine-tuning with long CoT datasets.
 
-3. **Instruction Following**:  
-   Improved capabilities in understanding and executing complex instructions while generating long texts (up to 4K tokens).
+2. **Enhanced Instruction Following**:  
+   Taurus-Opus-7B excels in generating long, coherent outputs (up to 4K tokens), understanding structured data, and producing structured outputs like JSON.
 
-4. **Structured Data Processing**:  
-   Excels at analyzing tables, JSON, and other structured data formats, ensuring accurate and structured outputs.
+3. **Lightweight Efficiency**:  
+   Its 7B parameter size makes it more resource-efficient compared to larger models while retaining high-quality performance for reasoning and content generation tasks.
 
-5. **Multilingual Proficiency**:  
-   Supports 20+ languages, maintaining accuracy and fluency in common languages such as English, Chinese, Spanish, and French.
+4. **Long-Context Support**:  
+   Offers support for long contexts of up to 64K tokens, enabling the handling of large datasets or extended conversations.
 
-6. **Streamlined Long-Context Support**:  
-   Supports up to 64K tokens, providing robust contextual understanding for long-chain reasoning tasks.
+5. **Multilingual Proficiency**:  
+   The model supports 20+ languages, including English, Spanish, French, German, Portuguese, Chinese, Japanese, and more, making it suitable for global applications.
 
 # **Quickstart with transformers**
 
+Here’s a code snippet to load **Taurus-Opus-7B** using the `transformers` library:
+
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "prithivMLmods/Taurus-Opus-7B-Elite"
+model_name = "your-organization/Taurus-Opus-7B"
 
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -52,9 +52,9 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-prompt = "Explain why reasoning is critical in solving complex problems."
+prompt = "Explain the importance of chain-of-thought reasoning in large language models."
 messages = [
-    {"role": "system", "content": "You are Taurus, an advanced AI assistant optimized for reasoning and problem-solving."},
+    {"role": "system", "content": "You are a helpful assistant with expertise in logical reasoning and problem-solving."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
@@ -66,7 +66,7 @@ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 
 generated_ids = model.generate(
     **model_inputs,
-    max_new_tokens=256
+    max_new_tokens=512
 )
 generated_ids = [
     output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
@@ -74,37 +74,39 @@ 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.
+1. **Reasoning and Context Understanding**:  
+   Taurus-Opus-7B is tailored for complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction.
 
-2. **Mathematical Reasoning**:  
-   Adept at solving mathematical problems and calculations, making it ideal for STEM applications.
+2. **Mathematical Problem-Solving**:  
+   Designed for advanced mathematical reasoning and calculations, making it valuable for education, research, and engineering tasks.
 
 3. **Code Assistance**:  
-   Provides support for generating, debugging, and optimizing code in a variety of programming languages.
+   Provides robust coding support, including writing, debugging, and optimizing code across multiple programming languages.
+
+4. **Data Analysis**:  
+   Excels in analyzing structured data and generating structured outputs, aiding automation workflows and data-driven insights.
 
-4. **Multilingual Tasks**:  
-   Enables global applications, including multilingual content generation, translation, and conversational AI.
+5. **Multilingual Support**:  
+   Facilitates applications such as multilingual chatbots, content generation, and translation in 20+ languages.
 
-5. **Content Generation**:  
-   Generates high-quality long-form text for reports, articles, and other professional documents.
+6. **Extended Content Generation**:  
+   Suitable for generating detailed reports, articles, and instructional guides, handling outputs up to 4K tokens.
 
 # **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.
+1. **Hardware Requirements**:  
+   While more efficient than larger models, Taurus-Opus-7B still requires high-memory GPUs or TPUs for optimal performance.
 
-2. **Hardware Requirements**:  
-   Though lighter than larger models, it still requires a GPU or high-performance CPU for optimal performance.
+2. **Language Quality Variations**:  
+   Output quality may vary across supported languages, especially for less commonly used languages.
 
-3. **Multilingual Accuracy**:  
-   Performance may vary for less-resourced languages, with minor inaccuracies in nuanced translations.
+3. **Creativity Limitations**:  
+   The model may sometimes generate repetitive or inconsistent results in creative or highly subjective tasks.
 
-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.
+4. **Real-Time Knowledge Constraints**:  
+   The model lacks awareness of events or knowledge updates beyond its training data.
 
-5. **Prompt Sensitivity**:  
-   Requires well-structured prompts for best performance, necessitating some user familiarity with prompt design.
+5. **Prompt Dependency**:  
+   Results heavily depend on the specificity and clarity of input prompts, requiring well-structured queries for the best performance.