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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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- text-generation-inference
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- math
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- code
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- trl
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- sft
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model-index:
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- name: Epimetheus-14B-Axo
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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: 55.46
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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%2FEpimetheus-14B-Axo
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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: 51.46
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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%2FEpimetheus-14B-Axo
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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: 41.01
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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%2FEpimetheus-14B-Axo
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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: 19.02
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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%2FEpimetheus-14B-Axo
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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: 19.71
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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%2FEpimetheus-14B-Axo
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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: 47.82
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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%2FEpimetheus-14B-Axo
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name: Open LLM Leaderboard
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---
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# **Epimetheus-14B-Axo**
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> Epimetheus-14B-Axo 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.
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## **Key Improvements**
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1. **Enhanced General Knowledge**: The model provides broad knowledge across various domains, improving capabilities in answering questions accurately and generating coherent responses.
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2. **Improved Instruction Following**: Significant advancements in understanding and following complex instructions, generating structured responses, and maintaining coherence over extended interactions.
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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.
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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.
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5. **Multilingual Proficiency**: Supports over 29 languages, including English, Chinese, 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 is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and generate content:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Epimetheus-14B-Axo"
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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 = "What are the key principles of general-purpose AI?"
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messages = [
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{"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
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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. **General-Purpose Reasoning**:
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Designed for broad applicability, assisting with logical reasoning, answering diverse questions, and solving general knowledge problems.
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2. **Educational and Informational Assistance**:
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Suitable for providing explanations, summaries, and research-based responses for students, educators, and general users.
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3. **Conversational AI and Chatbots**:
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Ideal for building intelligent conversational agents that require contextual understanding and dynamic response generation.
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4. **Multilingual Applications**:
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Supports global communication, translations, and multilingual content generation.
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5. **Structured Data Processing**:
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Capable of analyzing and generating structured outputs, such as tables and JSON, useful for data science and automation.
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6. **Long-Form Content Generation**:
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Can generate extended responses, including articles, reports, and guides, maintaining coherence over large text outputs.
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## **Limitations**
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1. **Hardware Requirements**:
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Requires high-memory GPUs or TPUs due to its large parameter size and long-context support.
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2. **Potential Bias in Responses**:
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While designed to be neutral, outputs may still reflect biases present in training data.
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3. **Inconsistent Outputs in Creative Tasks**:
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May produce variable results in storytelling and highly subjective topics.
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4. **Limited Real-World Awareness**:
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Does not have access to real-time events beyond its training cutoff.
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5. **Error Propagation in Extended Outputs**:
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Minor errors in early responses may affect overall coherence in long-form outputs.
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6. **Prompt Sensitivity**:
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The effectiveness of responses may depend on how well the input prompt is structured.
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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__Epimetheus-14B-Axo-details)!
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Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FEpimetheus-14B-Axo&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** | 39.08|
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|IFEval (0-Shot) | 55.46|
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|BBH (3-Shot) | 51.46|
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|MATH Lvl 5 (4-Shot)| 41.01|
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|GPQA (0-shot) | 19.02|
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|MuSR (0-shot) | 19.71|
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|MMLU-PRO (5-shot) | 47.82|
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