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README.md
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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library_name: transformers
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---
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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library_name: transformers
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---
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# **Maverick-1-3B Model Card**
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## **Model Overview**
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**Maverick-1-3B** is a 3.09-billion-parameter causal language model fine-tuned from Qwen2.5-3B-Instruct. This model is designed to excel in various natural language processing tasks, offering enhanced reasoning and instruction-following capabilities.
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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:** 3.09 billion total (2.77 billion non-embedding)
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- **Layers:** 36
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- **Attention Heads:** 16 for query and 2 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 32,768 tokens
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- **Languages Supported:** Primarily English, with basic support for other languages
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- **License:** MIT
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## **Training Details**
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Maverick-1-3B 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 dataset focused on instruction-following and general NLP tasks. This approach aimed to enhance the model's performance in complex reasoning and academic tasks.
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## **Intended Use**
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Maverick-1-3B is designed for a range of applications, including but not limited to:
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- **General NLP Tasks:** Engaging in text completion, summarization, and question-answering tasks.
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- **Academic Assistance:** Providing support for tutoring, essay composition, and research inquiries.
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- **Data Analysis:** Offering insights and interpretations of data-centric queries.
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While Maverick-1-3B is a powerful tool for various applications, it is not intended for real-time, safety-critical systems or for processing sensitive personal information.
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## **How to Use**
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To utilize Maverick-1-3B, 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-1-3B 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-1-3B"
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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-1-3B may exhibit biases present in its training data. Users should critically assess outputs, especially in sensitive contexts.
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- **Knowledge Cutoff:** The model's knowledge is current up to August 2024. It may not be aware of events or developments occurring after this date.
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- **Language Support:** While primarily trained on English data, performance in other languages may be inconsistent.
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## **Acknowledgements**
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Maverick-1-3B builds upon the work of the Qwen team. Gratitude is also extended to the open-source AI community for their contributions to tools and frameworks that facilitated the development of Maverick-1-3B.
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## **License**
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Maverick-1-3B is released under the MIT License, permitting wide usage with proper attribution.
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## **Contact**
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- Email: maverick@aayanmishra.com
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