| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: |
| - prithivMLmods/Calcium-Opus-14B-Elite2 |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - SFT |
| - Opus |
| - R1 |
| - trl |
| - CoT |
| - text-generation-inference |
| - rL |
| model-index: |
| - name: Calcium-Opus-14B-Elite2-R1 |
| 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: 63.26 |
| name: averaged accuracy |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2-R1 |
| 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: 47.34 |
| name: normalized accuracy |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2-R1 |
| 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: 29.83 |
| name: exact match |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2-R1 |
| 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: 18.79 |
| name: acc_norm |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2-R1 |
| 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: 21.42 |
| name: acc_norm |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2-R1 |
| 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: 47.2 |
| name: accuracy |
| source: |
| url: >- |
| https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite2-R1 |
| name: Open LLM Leaderboard |
| --- |
|  |
|
|
| # **Calcium-Opus-14B-Elite2-R1** |
|
|
| Calcium-Opus-14B-Elite2-R1 is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. It has been fine-tuned on a **synthetic dataset based on DeepSeek R1**, further optimizing its chain-of-thought (CoT) reasoning and logical problem-solving abilities. The model demonstrates significant improvements in context understanding, structured data processing, and long-context comprehension, making it ideal for complex reasoning tasks, instruction-following, and text generation. |
|
|
| ### **Key Improvements** |
| 1. **Enhanced Knowledge and Expertise**: Improved mathematical reasoning, coding proficiency, and structured data processing. |
| 2. **Fine-Tuned Instruction Following**: Optimized for precise responses, structured outputs (e.g., JSON), and generating long texts (8K+ tokens). |
| 3. **Greater Adaptability**: Better role-playing capabilities and resilience to diverse system prompts. |
| 4. **Long-Context Support**: Handles up to **128K tokens** and generates up to **8K tokens** per output. |
| 5. **Multilingual Proficiency**: Supports over **29 languages**, including Chinese, English, French, Spanish, Portuguese, German, and more. |
|
|
| ### **Quickstart with Transformers** |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_name = "prithivMLmods/Calcium-Opus-14B-Elite2-R1" |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype="auto", |
| device_map="auto", |
| trust_remote_code=True |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| |
| prompt = "Give me a short introduction to large language models." |
| messages = [ |
| {"role": "system", "content": "You are an advanced AI assistant with expert-level reasoning and knowledge."}, |
| {"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] |
| print(response) |
| ``` |
|
|
| ### **Intended Use** |
| - **Advanced Reasoning & Context Understanding**: Designed for logical deduction, multi-step problem-solving, and complex knowledge-based tasks. |
| - **Mathematical & Scientific Problem-Solving**: Enhanced capabilities for calculations, theorem proving, and scientific queries. |
| - **Code Generation & Debugging**: Generates and optimizes code across multiple programming languages. |
| - **Structured Data Analysis**: Processes tables, JSON, and structured outputs, making it ideal for data-centric tasks. |
| - **Multilingual Applications**: High proficiency in over 29 languages, enabling global-scale applications. |
| - **Extended Content Generation**: Supports detailed document writing, research reports, and instructional guides. |
|
|
| ### **Limitations** |
| 1. **High Computational Requirements**: Due to its **14B parameters** and **128K context support**, it requires powerful GPUs or TPUs for efficient inference. |
| 2. **Language-Specific Variability**: Performance may vary across supported languages, especially for low-resource languages. |
| 3. **Potential Error Accumulation**: Long-text generation can sometimes introduce inconsistencies over extended outputs. |
| 4. **Limited Real-World Awareness**: Knowledge is restricted to training data and may not reflect recent world events. |
| 5. **Prompt Sensitivity**: Outputs can depend on the specificity and clarity of the input prompt. |
| # [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-Elite2-R1-details)! |
| Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-14B-Elite2-R1&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! |
|
|
| | Metric |Value (%)| |
| |-------------------|--------:| |
| |**Average** | 37.97| |
| |IFEval (0-Shot) | 63.26| |
| |BBH (3-Shot) | 47.34| |
| |MATH Lvl 5 (4-Shot)| 29.83| |
| |GPQA (0-shot) | 18.79| |
| |MuSR (0-shot) | 21.42| |
| |MMLU-PRO (5-shot) | 47.20| |
