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:
-
Math-Optimized Capabilities:
The model is designed to handle complex mathematical problems, step-by-step calculations, and reasoning tasks. -
Instruction-Tuned:
Fine-tuned for better adherence to structured queries and task-oriented prompts, enabling clear and concise outputs. -
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:
- Download all model files, ensuring the PyTorch weights and tokenizer configurations are included.
- Load the model in your Python environment using frameworks like PyTorch or Hugging Face Transformers.
- Use the provided configurations (
config.jsonandgeneration_config.json) for optimal inference.