Improve language tag
Browse filesHi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.
README.md
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---
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license: apache-2.0
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language:
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|MMLU-PRO (5-shot) | 47.80|
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---
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license: apache-2.0
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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base_model:
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- Qwen/Qwen2.5-14B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- opus
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- elite
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- 14B
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- calcium
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- qwq
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- trl
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model-index:
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- name: Calcium-Opus-14B-Elite
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: wis-k/instruction-following-eval
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split: train
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 60.64
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name: averaged accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: SaylorTwift/bbh
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split: test
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 46.53
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: lighteval/MATH-Hard
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split: test
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 37.08
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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split: train
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 16.44
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 20.95
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 47.85
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-14B-Elite
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name: Open LLM Leaderboard
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---
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# **Calcium-Opus-14B-Elite**
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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.
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# **Open-Evals**
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| Rank | Model | Average | IFEval | BBH | MATH | GPQA | MUSR | MMLU | CO₂ Consumption | Dated |
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|------|-----------------------------------------|---------|--------|-------|-------|-------|-------|-------|-----------------|---------|
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| 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
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Key improvements include:
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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.
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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.
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3. **Better Adaptability**: The model is more resilient to diverse system prompts, enabling enhanced role-playing implementations and condition-setting for chatbots.
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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.
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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.
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# **Quickstart with transformers**
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Calcium-Opus-14B-Elite"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Give me a short introduction to large language model."
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messages = [
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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# **Intended Use**
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1. **Reasoning and Context Understanding**:
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Designed to assist with complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction and critical thinking.
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2. **Mathematical Problem-Solving**:
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Specialized for performing advanced mathematical reasoning and calculations, making it suitable for educational, scientific, and research-oriented applications.
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3. **Code Generation and Debugging**:
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Offers robust support for coding tasks, including writing, debugging, and optimizing code in various programming languages, ideal for developers and software engineers.
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4. **Structured Data Analysis**:
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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.
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5. **Multilingual Applications**:
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Supports over 29 languages, making it versatile for global applications like multilingual chatbots, content generation, and translations.
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6. **Extended Content Generation**:
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Capable of generating long-form content (over 8K tokens), useful for writing reports, articles, and creating detailed instructional guides.
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# **Limitations**
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1. **Hardware Requirements**:
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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.
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2. **Potential Bias in Multilingual Outputs**:
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While it supports 29 languages, the quality and accuracy of outputs may vary depending on the language, especially for less-resourced languages.
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3. **Inconsistent Outputs for Creative Tasks**:
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The model may occasionally produce inconsistent or repetitive results in creative writing, storytelling, or highly subjective tasks.
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4. **Limited Real-World Awareness**:
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It lacks real-time knowledge of current events beyond its training cutoff, which may limit its ability to respond accurately to the latest information.
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5. **Error Propagation in Long-Text Outputs**:
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In generating long texts, minor errors in early outputs can sometimes propagate, reducing the overall coherence and accuracy of the response.
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6. **Dependency on High-Quality Prompts**:
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Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Calcium-Opus-14B-Elite-details)!
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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)!
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| Metric |Value (%)|
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|-------------------|--------:|
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|**Average** | 40.08|
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|IFEval (0-Shot) | 60.52|
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|BBH (3-Shot) | 46.93|
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|MATH Lvl 5 (4-Shot)| 47.89|
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|GPQA (0-shot) | 16.55|
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| 236 |
+
|MuSR (0-shot) | 20.78|
|
| 237 |
|MMLU-PRO (5-shot) | 47.80|
|