---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-14B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- qwen
- opus
---
![opus.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/BELYApcX2oNMRsOW6nIyR.gif)

# **Calcium-Opus-14B-Elite**

Calcium-Opus-14B-Elite 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 | Type | Model                                    | Average | IFEval | BBH   | MATH  | GPQA  | MUSR  | MMLU  | CO₂ Consumption | Dated |
|------|------|-----------------------------------------|---------|--------|-------|-------|-------|-------|-------|-----------------|---------|
| 108  | 🔶   | [prithivMLmods/Calcium-Opus-14B-Elite](https://huggingface.co/prithivMLmods/Calcium-Opus-14B-Elite)   | 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-Elite"

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.