--- license: apache-2.0 language: - en base_model: - prithivMLmods/Megatron-Corpus-14B-Exp pipeline_tag: text-generation library_name: transformers tags: - Coding - Math model-index: - name: Megatron-Corpus-14B-Exp.v2 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: 48.7 name: averaged accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2 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: 46.79 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2 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: 25.3 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2 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: 12.3 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2 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: 15.36 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2 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: 42.33 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2 name: Open LLM Leaderboard --- ![corpus2.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/XUiPFzD6nKvXqkCTX94Z5.gif) # **Megatron-Corpus-14B-Exp.v2** Megatron-Corpus-14B-Exp.v2 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 math corpus, 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. **Advanced Reasoning & Logic**: Optimized for multi-step problem-solving, logical deduction, and contextual analysis. 2. **Fine-Tuned Instruction Following**: Generates precise responses, structured outputs (e.g., JSON), and extended long-form text (8K+ tokens). 3. **Greater Adaptability**: Excels in role-playing, multi-turn dialogues, and 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/Megatron-Corpus-14B-Exp.v2" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Explain the concept of logical reasoning in AI." messages = [ {"role": "system", "content": "You are an expert AI assistant specialized in reasoning and logic."}, {"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 Logical & Analytical Reasoning**: Designed for problem-solving, multi-step deductions, and cognitive reasoning tasks. - **Mathematical & Scientific Computation**: Supports theorem proving, complex calculations, and scientific knowledge retrieval. - **Code Generation & Debugging**: Generates optimized code, detects errors, and improves programming workflows. - **Structured Data Analysis**: Processes tables, JSON, and structured formats for data-centric applications. - **Multilingual Reasoning & Translation**: High proficiency across **29+ languages** for international applications. - **Extended Text Generation**: Capable of generating research papers, instructional guides, and in-depth reports. ### **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 differ across supported languages, especially for low-resource languages. 3. **Potential Error Accumulation**: Long-form text generation can 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**: The quality of responses depends 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__Megatron-Corpus-14B-Exp.v2-details)! Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FMegatron-Corpus-14B-Exp.v2&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)! | Metric |Value (%)| |-------------------|--------:| |**Average** | 31.80| |IFEval (0-Shot) | 48.70| |BBH (3-Shot) | 46.79| |MATH Lvl 5 (4-Shot)| 25.30| |GPQA (0-shot) | 12.30| |MuSR (0-shot) | 15.36| |MMLU-PRO (5-shot) | 42.33|