--- license: apache-2.0 pipeline_tag: text-generation language: - en - zh base_model: - prithivMLmods/Megatron-Corpus-14B-Exp library_name: transformers tags: - text-generation-inference - math - trl - reasoning - QwQ model-index: - name: Magellanic-Opus-14B-Exp 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: 68.66 name: averaged accuracy source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMagellanic-Opus-14B-Exp 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: 48 name: normalized accuracy source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMagellanic-Opus-14B-Exp 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: 37.99 name: exact match source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMagellanic-Opus-14B-Exp 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: 16.55 name: acc_norm source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMagellanic-Opus-14B-Exp 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: 21.64 name: acc_norm source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMagellanic-Opus-14B-Exp 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: 47.47 name: accuracy source: url: >- https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMagellanic-Opus-14B-Exp name: Open LLM Leaderboard --- ![asd.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/zpvdrEWdThcnX0zeSyA-r.png) # **Magellanic-Opus-14B-Exp** Magellanic-Opus-14B-Exp is based on the Qwen 2.5 14B modality architecture, designed to enhance the reasoning capabilities of 14B-parameter models. This model is optimized for general-purpose reasoning and answering, excelling in contextual understanding, logical deduction, and multi-step problem-solving. It has been fine-tuned using a long chain-of-thought reasoning model and specialized datasets to improve comprehension, structured responses, and conversational intelligence. ## **Key Improvements** 1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses. 2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions. 3. **Versatile Adaptability**: More resilient to diverse prompts, enhancing its ability to handle a wide range of topics and conversation styles, including open-ended and structured inquiries. 4. **Long-Context Support**: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, making it ideal for detailed responses. 5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. ## **Quickstart with transformers** Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Magellanic-Opus-14B-Exp" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "What are the key principles of general-purpose AI?" messages = [ {"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."}, {"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. **General-Purpose Reasoning**: Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems. 2. **Educational and Informational Assistance**: Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users. 3. **Conversational AI and Chatbots**: Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation. 4. **Multilingual Applications**: Supports global communication, translations, and multilingual content generation. 5. **Structured Data Processing**: Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation. 6. **Long-Form Content Generation**: Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs. ## **Limitations** 1. **Hardware Requirements**: Requires high-memory GPUs or TPUs due to its large parameter size and long-context support. 2. **Potential Bias in Responses**: While designed to be neutral, outputs may still reflect biases present in training data. 3. **Inconsistent Outputs in Creative Tasks**: May produce variable results in storytelling and highly subjective topics. 4. **Limited Real-World Awareness**: Does not have access to real-time events beyond its training cutoff. 5. **Error Propagation in Extended Outputs**: Minor errors in early responses may affect overall coherence in long-form outputs. 6. **Prompt Sensitivity**: The effectiveness of responses may depend on how well the input prompt is structured. # [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__Magellanic-Opus-14B-Exp-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FMagellanic-Opus-14B-Exp&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! | Metric |Value (%)| |-------------------|--------:| |**Average** | 40.05| |IFEval (0-Shot) | 68.66| |BBH (3-Shot) | 48.00| |MATH Lvl 5 (4-Shot)| 37.99| |GPQA (0-shot) | 16.55| |MuSR (0-shot) | 21.64| |MMLU-PRO (5-shot) | 47.47|