Update README.md

README.md

@@ -11,4 +11,93 @@ tags:
 - Calcium
 - Opus
 - 14B
----
+---
+
+![e4.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/XZ2mwzGAOdtV3gQ-DvEU_.gif)
+
+# **Calcium-Opus-14B-Elite4**
+
+Calcium-Opus-14B-Elite4 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. These models have proven effective in context understanding, reasoning, and mathematical problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets, with a focus on chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
+
+Key improvements include:  
+1. **Enhanced Knowledge and Expertise**: The model demonstrates significantly more knowledge and greatly improved capabilities in coding and mathematics, thanks to specialized expert models in these domains.  
+2. **Improved Instruction Following**: It shows significant advancements in following instructions, generating long texts (over 8K tokens), understanding structured data (e.g., tables), and producing structured outputs, especially in JSON format.  
+3. **Better Adaptability**: The model is more resilient to diverse system prompts, enabling enhanced role-playing implementations and condition-setting for chatbots.  
+4. **Long-Context Support**: It offers long-context support of up to 128K tokens and can generate up to 8K tokens in a single output.  
+5. **Multilingual Proficiency**: The model supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+
+# **Quickstart with transformers**
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Calcium-Opus-14B-Elite4"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
+    {"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. **Reasoning and Context Understanding**:  
+   Designed to assist with complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction and critical thinking.  
+
+2. **Mathematical Problem-Solving**:  
+   Specialized for performing advanced mathematical reasoning and calculations, making it suitable for educational, scientific, and research-oriented applications.  
+
+3. **Code Generation and Debugging**:  
+   Offers robust support for coding tasks, including writing, debugging, and optimizing code in various programming languages, ideal for developers and software engineers.  
+
+4. **Structured Data Analysis**:  
+   Excels in processing and analyzing structured data, such as tables and JSON, and generating structured outputs, which is useful for data analysts and automation workflows.  
+
+5. **Multilingual Applications**:  
+   Supports over 29 languages, making it versatile for global applications like multilingual chatbots, content generation, and translations.  
+
+6. **Extended Content Generation**:  
+   Capable of generating long-form content (over 8K tokens), useful for writing reports, articles, and creating detailed instructional guides.  
+
+# **Limitations**  
+1. **Hardware Requirements**:  
+   Due to its 20B parameter size and support for long-context inputs, running the model requires significant computational resources, including high-memory GPUs or TPUs.  
+
+2. **Potential Bias in Multilingual Outputs**:  
+   While it supports 29 languages, the quality and accuracy of outputs may vary depending on the language, especially for less-resourced languages.  
+
+3. **Inconsistent Outputs for Creative Tasks**:  
+   The model may occasionally produce inconsistent or repetitive results in creative writing, storytelling, or highly subjective tasks.  
+
+4. **Limited Real-World Awareness**:  
+   It lacks real-time knowledge of current events beyond its training cutoff, which may limit its ability to respond accurately to the latest information.  
+
+5. **Error Propagation in Long-Text Outputs**:  
+   In generating long texts, minor errors in early outputs can sometimes propagate, reducing the overall coherence and accuracy of the response.  
+
+6. **Dependency on High-Quality Prompts**:  
+   Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.