Serpens Opus 14B Exp

🚀 Serpens-Opus-14B-Exp

Serpens-Opus-14B-Exp is based on the Qwen 2.5 14B modality architecture, aiming to enhance the reasoning capabilities of 14B-parameter models. It excels in general-purpose reasoning and answering, with strong performance in contextual understanding, logical deduction, and multi-step problem-solving.

✨ Features

• Enhanced General Knowledge: The model offers extensive knowledge across diverse domains, enabling accurate question - answering and coherent response generation.
• Improved Instruction Following: It has made significant progress in understanding and executing complex instructions, generating structured responses, and maintaining coherence during long - term interactions.
• Versatile Adaptability: More resilient to various prompts, it can handle a wide range of topics and conversation styles, including open - ended and structured inquiries.
• Long - Context Support: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output, suitable for detailed responses.
• Multilingual Proficiency: Supports over 29 languages, such as English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, etc.

📦 Installation

This model uses the transformers library. You can install it via the following command:

pip install transformers

💻 Usage Examples

Basic Usage

Here is a code snippet with apply_chat_template to show you how to load the tokenizer and model and generate content:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Serpens-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]

📚 Documentation

Intended Use

1. General - Purpose Reasoning: Designed for wide - ranging applications, it can assist with logical reasoning, answer diverse questions, and solve 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, while maintaining coherence in large - scale text outputs.

Limitations

1. Hardware Requirements: Due to its large parameter size and long - context support, it requires high - memory GPUs or TPUs.
2. Potential Bias in Responses: Although designed to be neutral, outputs may still reflect biases in the 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 the overall coherence of long - form outputs.
6. Prompt Sensitivity: The effectiveness of responses may depend on the quality of the input prompt structure.

📄 License

This model is licensed under the Apache 2.0 license.