Tadpole Opus 14B Exp

🚀 Tadpole-Opus-14B-Exp

Tadpole-Opus-14B-Exp is based on the Qwen 2.5 14B modality architecture. It aims 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.

🚀 Quick Start

Tadpole-Opus-14B-Exp can be quickly utilized with the transformers library. 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/Tadpole-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]

✨ Features

Key improvements include:

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.

📚 Documentation

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.

📄 License

This model is under the Apache - 2.0 license.

📊 Model Information

📈 Evaluation Results

Open LLM Leaderboard Evaluation Results Detailed results can be found here! Summarized results can be found here!

Model Index

• Name: Tadpole - Opus - 14B - Exp
  • Results:
    • Task 1:
      • Task Type: text - generation
      • Dataset: IFEval (0 - Shot) (wis - k/instruction - following - eval, train split, 0 - shot)
      • Metrics: averaged accuracy of 57.5
      • Source: Open LLM Leaderboard
    • Task 2:
      • Task Type: text - generation
      • Dataset: BBH (3 - Shot) (SaylorTwift/bbh, test split, 3 - shot)
      • Metrics: normalized accuracy of 47.78
      • Source: Open LLM Leaderboard
    • Task 3:
      • Task Type: text - generation
      • Dataset: MATH Lvl 5 (4 - Shot) (lighteval/MATH - Hard, test split, 4 - shot)
      • Metrics: exact match of 31.34
      • Source: Open LLM Leaderboard
    • Task 4:
      • Task Type: text - generation
      • Dataset: GPQA (0 - shot) (Idavidrein/gpqa, train split, 0 - shot)
      • Metrics: acc_norm of 18.12
      • Source: Open LLM Leaderboard
    • Task 5:
      • Task Type: text - generation
      • Dataset: MuSR (0 - shot) (TAUR - Lab/MuSR, 0 - shot)
      • Metrics: acc_norm of 18.51
      • Source: Open LLM Leaderboard
    • Task 6:
      • Task Type: text - generation
      • Dataset: MMLU - PRO (5 - shot) (TIGER - Lab/MMLU - Pro, main config, test split, 5 - shot)
      • Metrics: accuracy of 48.03
      • Source: Open LLM Leaderboard