| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - Qwen/Qwen2.5-14B-Instruct |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - opus |
| - elite |
| - 14B |
| - calcium |
| - qwq |
| - trl |
| model-index: |
| - name: Calcium-Opus-14B-Elite |
| results: |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: IFEval (0-Shot) |
| type: wis-k/instruction-following-eval |
| split: train |
| args: |
| num_few_shot: 0 |
| metrics: |
| - type: inst_level_strict_acc and prompt_level_strict_acc |
| value: 60.64 |
| name: averaged accuracy |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite |
| name: Open LLM Leaderboard |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: BBH (3-Shot) |
| type: SaylorTwift/bbh |
| split: test |
| args: |
| num_few_shot: 3 |
| metrics: |
| - type: acc_norm |
| value: 46.53 |
| name: normalized accuracy |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite |
| name: Open LLM Leaderboard |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: MATH Lvl 5 (4-Shot) |
| type: lighteval/MATH-Hard |
| split: test |
| args: |
| num_few_shot: 4 |
| metrics: |
| - type: exact_match |
| value: 37.08 |
| name: exact match |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite |
| name: Open LLM Leaderboard |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: GPQA (0-shot) |
| type: Idavidrein/gpqa |
| split: train |
| args: |
| num_few_shot: 0 |
| metrics: |
| - type: acc_norm |
| value: 16.44 |
| name: acc_norm |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite |
| name: Open LLM Leaderboard |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: MuSR (0-shot) |
| type: TAUR-Lab/MuSR |
| args: |
| num_few_shot: 0 |
| metrics: |
| - type: acc_norm |
| value: 20.95 |
| name: acc_norm |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite |
| name: Open LLM Leaderboard |
| - task: |
| type: text-generation |
| name: Text Generation |
| dataset: |
| name: MMLU-PRO (5-shot) |
| type: TIGER-Lab/MMLU-Pro |
| config: main |
| split: test |
| args: |
| num_few_shot: 5 |
| metrics: |
| - type: acc |
| value: 47.85 |
| name: accuracy |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite |
| name: Open LLM Leaderboard |
| --- |
|  |
|
|
| # **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 | 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. |
|
|
| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Calcium-Opus-14B-Elite-details)! |
| Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-14B-Elite&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! |
|
|
| | Metric |Value (%)| |
| |-------------------|--------:| |
| |**Average** | 40.08| |
| |IFEval (0-Shot) | 60.52| |
| |BBH (3-Shot) | 46.93| |
| |MATH Lvl 5 (4-Shot)| 47.89| |
| |GPQA (0-shot) | 16.55| |
| |MuSR (0-shot) | 20.78| |
| |MMLU-PRO (5-shot) | 47.80| |
