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README.md
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language:
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- en
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---
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language:
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- en
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---
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# **Maverick-1-14B Model Card**
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## **Model Overview**
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**Maverick-1-14B** is a 14.0-billion-parameter causal language model fine-tuned from Qwen2.5-14B-Instruct. This model is designed to provide highly fluent, contextually aware, and logically sound outputs across a broad range of NLP and reasoning tasks. It balances instruction-following with generative flexibility.
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## **Model Details**
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- **Model Developer:** Aayan Mishra
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- **Model Type:** Causal Language Model
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- **Architecture:** Transformer with Rotary Position Embeddings (RoPE), SwiGLU activation, RMSNorm, Attention QKV bias, and tied word embeddings
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- **Parameters:** 14.0 billion total (12.84 billion non-embedding)
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- **Layers:** 40
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- **Attention Heads:** 40 for query and 4 for key-value (Grouped Query Attention)
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- **Vocabulary Size:** Approximately 151,646 tokens
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- **Context Length:** Supports up to 131,072 tokens
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- **Languages Supported:** Over 29 languages, including strong performance in English, Chinese, and multilingual instruction tasks
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- **License:** MIT
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## **Training Details**
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Maverick-1-14B was fine-tuned using the Unsloth framework on a single NVIDIA A100 GPU. The fine-tuning process spanned approximately 90 minutes over 60 epochs, utilizing a curated instruction-tuned dataset. It is tailored for generalist NLP performance with a focus on reasoning, alignment, and fluency.
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## **Intended Use**
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Maverick-1-14B is ideal for a wide variety of tasks, including:
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- **Instruction Following:** Handling complex prompts with step-by-step logical output
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- **Writing Assistance:** Generating essays, emails, and coherent narratives
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- **NLP Tasks:** Summarization, question answering, translation, and text classification
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- **STEM Support:** Reasoning through academic and technical content
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While Maverick-1-14B is a versatile model, it is not intended for safety-critical applications or the handling of private, sensitive information.
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## **How to Use**
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To utilize Maverick-1-14B, ensure that you have the latest version of the `transformers` library installed:
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```bash
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pip install transformers
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```
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Here's an example of how to load the Maverick-1-14B model and generate a response:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Spestly/Maverick-1-14B"
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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 = "Explain the concept of entropy in thermodynamics."
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messages = [
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{"role": "system", "content": "You are Maverick, an AI assistant designed to be helpful."},
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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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print(response)
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```
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## **Limitations**
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Users should be aware of the following limitations:
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- **Biases:** Maverick-1-14B may reflect biases from its pretraining and fine-tuning data. Outputs should be reviewed for fairness and accuracy.
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- **Knowledge Cutoff:** The model's knowledge is current as of August 2024.
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- **Multilingual Performance:** Performance varies by language, with strongest capabilities in English and aligned datasets.
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## **Acknowledgements**
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Maverick-1-14B builds upon the Qwen2.5-14B foundation. Special thanks to the open-source ecosystem and Unsloth for enabling efficient fine-tuning workflows.
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## **License**
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Maverick-1-14B is released under the MIT License, permitting broad use and distribution with proper attribution.
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## **Contact**
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- Email: maverick@aayanmishra.com
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