--- 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 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 --- ![r1.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/iHEhTX2ZGk9wmBMIcueTf.gif) # **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|