QuantFactory/Math-IIO-7B-Instruct-GGUF

This is quantized version of prithivMLmods/Math-IIO-7B-Instruct created using llama.cpp

Original Model Card

Math IIO 7B Instruct

The Math IIO 7B Instruct is a fine-tuned language model based on the robust Qwen2.5-7B-Instruct architecture. This model has been specifically trained to excel in single-shot mathematical reasoning and instruction-based tasks, making it a reliable choice for educational, analytical, and problem-solving applications.

Key Features:

1. Math-Optimized Capabilities:
   The model is designed to handle complex mathematical problems, step-by-step calculations, and reasoning tasks.

2. Instruction-Tuned:
   Fine-tuned for better adherence to structured queries and task-oriented prompts, enabling clear and concise outputs.

3. Large Vocabulary:
   Equipped with an extensive tokenizer configuration and custom tokens to ensure precise mathematical notation support.

File Name	Size	Description	Upload Status
.gitattributes	1.57 kB	Git attributes configuration file	Uploaded
README.md	263 Bytes	README file with minimal details	Updated
added_tokens.json	657 Bytes	Custom added tokens for tokenizer	Uploaded
config.json	861 Bytes	Model configuration file	Uploaded
generation_config.json	281 Bytes	Configuration for text generation settings	Uploaded
merges.txt	1.82 MB	Merge rules for byte pair encoding tokenizer	Uploaded
pytorch_model-00001-of-00004.bin	4.88 GB	First part of model weights (PyTorch)	Uploaded (LFS)
pytorch_model-00002-of-00004.bin	4.93 GB	Second part of model weights (PyTorch)	Uploaded (LFS)
pytorch_model-00003-of-00004.bin	4.33 GB	Third part of model weights (PyTorch)	Uploaded (LFS)
pytorch_model-00004-of-00004.bin	1.09 GB	Fourth part of model weights (PyTorch)	Uploaded (LFS)
pytorch_model.bin.index.json	28.1 kB	Index JSON file for model weights	Uploaded
special_tokens_map.json	644 Bytes	Map of special tokens used by the tokenizer	Uploaded
tokenizer.json	11.4 MB	Tokenizer settings and vocab	Uploaded (LFS)
tokenizer_config.json	7.73 kB	Configuration for tokenizer	Uploaded
vocab.json	2.78 MB	Vocabulary for tokenizer	Uploaded

Training Details:

• Base Model: Qwen/Qwen2.5-7B-Instruct
• Dataset: Trained on Math-IIO-68K-Mini, a curated dataset with 68.8k high-quality examples focusing on mathematical instructions, equations, and logic-based queries.

Capabilities:

• Problem-Solving: Solves mathematical problems ranging from basic arithmetic to advanced calculus and linear algebra.
• Educational Use: Explains solutions step-by-step, making it a valuable teaching assistant.
• Analysis & Reasoning: Handles logical reasoning tasks and computational queries effectively.

How to Use:

1. Download all model files, ensuring the PyTorch weights and tokenizer configurations are included.
2. Load the model in your Python environment using frameworks like PyTorch or Hugging Face Transformers.
3. Use the provided configurations (config.json and generation_config.json) for optimal inference.