|
|
|
--- |
|
|
|
license: creativeml-openrail-m |
|
datasets: |
|
- AI-MO/NuminaMath-CoT |
|
language: |
|
- en |
|
base_model: |
|
- Qwen/Qwen2.5-7B-Instruct |
|
pipeline_tag: text-generation |
|
library_name: transformers |
|
tags: |
|
- Qwen2.5 |
|
- Ollama |
|
- Neumind |
|
- Math |
|
- Instruct |
|
- safetensors |
|
- pytorch |
|
- trl |
|
|
|
--- |
|
|
|
[](https://hf.co/QuantFactory) |
|
|
|
|
|
# QuantFactory/Neumind-Math-7B-Instruct-GGUF |
|
This is quantized version of [prithivMLmods/Neumind-Math-7B-Instruct](https://huggingface.co/prithivMLmods/Neumind-Math-7B-Instruct) created using llama.cpp |
|
|
|
# Original Model Card |
|
|
|
|
|
### Neumind-Math-7B-Instruct Model Files |
|
|
|
The **Neumind-Math-7B-Instruct** is a fine-tuned model based on **Qwen2.5-7B-Instruct**, optimized for mathematical reasoning, step-by-step problem-solving, and instruction-based tasks in the mathematics domain. The model is designed for applications requiring structured reasoning, numerical computations, and mathematical proof generation. |
|
|
|
| File Name | Size | Description | Upload Status | |
|
|------------------------------------|------------|------------------------------------------|----------------| |
|
| `.gitattributes` | 1.57 kB | Git attributes configuration file | Uploaded | |
|
| `README.md` | 265 Bytes | ReadMe file with basic information | Updated | |
|
| `added_tokens.json` | 657 Bytes | Additional token definitions | Uploaded | |
|
| `config.json` | 860 Bytes | Model configuration settings | Uploaded | |
|
| `generation_config.json` | 281 Bytes | Generation settings | Uploaded | |
|
| `merges.txt` | 1.82 MB | Tokenizer merge rules | Uploaded | |
|
| `pytorch_model-00001-of-00004.bin` | 4.88 GB | Model shard 1 of 4 | Uploaded (LFS) | |
|
| `pytorch_model-00002-of-00004.bin` | 4.93 GB | Model shard 2 of 4 | Uploaded (LFS) | |
|
| `pytorch_model-00003-of-00004.bin` | 4.33 GB | Model shard 3 of 4 | Uploaded (LFS) | |
|
| `pytorch_model-00004-of-00004.bin` | 1.09 GB | Model shard 4 of 4 | Uploaded (LFS) | |
|
| `pytorch_model.bin.index.json` | 28.1 kB | Model index JSON | Uploaded | |
|
| `special_tokens_map.json` | 644 Bytes | Mapping of special tokens | Uploaded | |
|
| `tokenizer.json` | 11.4 MB | Tokenizer configuration | Uploaded (LFS) | |
|
| `tokenizer_config.json` | 7.73 kB | Additional tokenizer settings | Uploaded | |
|
| `vocab.json` | 2.78 MB | Vocabulary for tokenization | Uploaded | |
|
|
|
--- |
|
|
|
### **Key Features:** |
|
|
|
1. **Mathematical Reasoning:** |
|
Specifically fine-tuned for solving mathematical problems, including arithmetic, algebra, calculus, and geometry. |
|
|
|
2. **Step-by-Step Problem Solving:** |
|
Provides detailed, logical solutions for complex mathematical tasks and demonstrates problem-solving methodologies. |
|
|
|
3. **Instructional Applications:** |
|
Tailored for use in educational settings, such as tutoring systems, math content creation, and interactive learning tools. |
|
|
|
--- |
|
|
|
### **Training Details:** |
|
- **Base Model:** [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) |
|
- **Dataset:** Trained on **AI-MO/NuminaMath-CoT**, a large dataset of mathematical problems and chain-of-thought (CoT) reasoning. The dataset contains **860k problems** across various difficulty levels, enabling the model to tackle a wide spectrum of mathematical tasks. |
|
|
|
--- |
|
|
|
### **Capabilities:** |
|
|
|
- **Complex Problem Solving:** |
|
Solves a wide range of mathematical problems, from basic arithmetic to advanced calculus and algebraic equations. |
|
|
|
- **Chain-of-Thought Reasoning:** |
|
Excels in step-by-step logical reasoning, making it suitable for tasks requiring detailed explanations. |
|
|
|
- **Instruction-Based Generation:** |
|
Ideal for generating educational content, such as worked examples, quizzes, and tutorials. |
|
|
|
--- |
|
|
|
### **Usage Instructions:** |
|
|
|
1. **Model Setup:** |
|
Download all model shards and the associated configuration files. Ensure the files are correctly placed for seamless loading. |
|
|
|
2. **Inference:** |
|
Load the model using frameworks like PyTorch and Hugging Face Transformers. Ensure the `pytorch_model.bin.index.json` file is in the same directory for shard-based loading. |
|
|
|
3. **Customization:** |
|
Adjust generation parameters using `generation_config.json` to optimize outputs for your specific application. |
|
--- |
|
|
|
### **Applications:** |
|
|
|
- **Education:** |
|
Interactive math tutoring, content creation, and step-by-step problem-solving tools. |
|
- **Research:** |
|
Automated theorem proving and symbolic mathematics. |
|
- **General Use:** |
|
Solving everyday mathematical queries and generating numerical datasets. |
|
--- |
|
|
