Update README.md

README.md

@@ -11,4 +11,101 @@ tags:
 - Cot
 - elite
 - calcium
----
+---
+# **Calcium-Opus-14B-Elite3**
+
+Calcium-Opus-14B-Elite3 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.
+
+# **Open-Evals**
+
+| Rank | Model                                                                                                 | Average | IFEval | BBH   | MATH  | GPQA  | MUSR  | MMLU  | CO₂ Consumption | Dated      |
+| ---- | ----------------------------------------------------------------------------------------------------- | ------- | ------ | ----- | ----- | ----- | ----- | ----- | --------------- | ---------- |
+| 108  | [prithivMLmods/Calcium-Opus-14B-Elite3](https://huggingface.co/prithivMLmods/Calcium-Opus-14B-Elite3) | 38.38   | 60.52  | 46.93 | 37.69 | 16.55 | 20.78 | 47.80 | 2.01            | 01/23/2025 |
+
+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-Elite3"
+
+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.
+