初始化项目，由ModelHub XC社区提供模型
Model: prithivMLmods/Neumind-Math-7B-Instruct-GGUF Source: Original Platform
This commit is contained in:
ModelHub XC
39
.gitattributes
vendored
Normal file
39
.gitattributes
vendored
Normal file
@@ -0,0 +1,39 @@
|
||||
*.7z filter=lfs diff=lfs merge=lfs -text
|
||||
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
||||
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
||||
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||
*.gz filter=lfs diff=lfs merge=lfs -text
|
||||
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
||||
*.model filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
*.npy filter=lfs diff=lfs merge=lfs -text
|
||||
*.npz filter=lfs diff=lfs merge=lfs -text
|
||||
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||
*.ot filter=lfs diff=lfs merge=lfs -text
|
||||
*.parquet filter=lfs diff=lfs merge=lfs -text
|
||||
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||
*.pickle filter=lfs diff=lfs merge=lfs -text
|
||||
*.pkl filter=lfs diff=lfs merge=lfs -text
|
||||
*.pt filter=lfs diff=lfs merge=lfs -text
|
||||
*.pth filter=lfs diff=lfs merge=lfs -text
|
||||
*.rar filter=lfs diff=lfs merge=lfs -text
|
||||
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar filter=lfs diff=lfs merge=lfs -text
|
||||
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||
*.tgz filter=lfs diff=lfs merge=lfs -text
|
||||
*.wasm filter=lfs diff=lfs merge=lfs -text
|
||||
*.xz filter=lfs diff=lfs merge=lfs -text
|
||||
*.zip filter=lfs diff=lfs merge=lfs -text
|
||||
*.zst filter=lfs diff=lfs merge=lfs -text
|
||||
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||
Neumind-Math-7B-Instruct.F16.gguf filter=lfs diff=lfs merge=lfs -text
|
||||
Neumind-Math-7B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
|
||||
Neumind-Math-7B-Instruct.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
|
||||
Neumind-Math-7B-Instruct.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
|
||||
3
Neumind-Math-7B-Instruct.F16.gguf
Normal file
3
Neumind-Math-7B-Instruct.F16.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a05237cf4be8b066d8e069779bc12807c8b79dc117f4cffaa988643b27de57f8
|
||||
size 15237853024
|
||||
3
Neumind-Math-7B-Instruct.Q4_K_M.gguf
Normal file
3
Neumind-Math-7B-Instruct.Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:dbec077907d5fd265b2b63b9ac00769b4193f120092c38a8e3fdd08fbada0122
|
||||
size 4683073376
|
||||
3
Neumind-Math-7B-Instruct.Q5_K_M.gguf
Normal file
3
Neumind-Math-7B-Instruct.Q5_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c36ab7882a1af7675d44ca3e62836caa69f31df1ec63765bc9d45f3c3f085147
|
||||
size 5444831072
|
||||
3
Neumind-Math-7B-Instruct.Q8_0.gguf
Normal file
3
Neumind-Math-7B-Instruct.Q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:872e55e284efadb0e6d822012b16eba5ea0d2bc4df28c938b71f54240234260e
|
||||
size 8098525024
|
||||
177
README.md
Normal file
177
README.md
Normal file
@@ -0,0 +1,177 @@
|
||||
---
|
||||
license: creativeml-openrail-m
|
||||
datasets:
|
||||
- AI-MO/NuminaMath-CoT
|
||||
language:
|
||||
- en
|
||||
base_model:
|
||||
- prithivMLmods/Neumind-Math-7B-Instruct
|
||||
pipeline_tag: text-generation
|
||||
library_name: transformers
|
||||
tags:
|
||||
- Qwen2.5
|
||||
- Ollama
|
||||
- Math
|
||||
- Neumind
|
||||
- 7B
|
||||
- Instruct
|
||||
---
|
||||
|
||||
### Neumind-Math-7B-Instruct-GGUF 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` | 1.81 kB | Git attributes configuration file | Uploaded |
|
||||
| `Neumind-Math-7B-Instruct.F16.gguf` | 15.2 GB | Model weights in FP16 precision | Uploaded (LFS) |
|
||||
| `Neumind-Math-7B-Instruct.Q4_K_M.gguf` | 4.68 GB | Quantized model (Q4) | Uploaded (LFS) |
|
||||
| `Neumind-Math-7B-Instruct.Q5_K_M.gguf` | 5.44 GB | Quantized model (Q5) | Uploaded (LFS) |
|
||||
| `Neumind-Math-7B-Instruct.Q8_0.gguf` | 8.1 GB | Quantized model (Q8) | Uploaded (LFS) |
|
||||
| `README.md` | 254 Bytes | Basic documentation for the model | Updated |
|
||||
| `config.json` | 31 Bytes | Minimal configuration for the model | 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-Instruct](https://huggingface.co/prithivMLmods/Neumind-Math-7B-Instruct)
|
||||
- **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.
|
||||
---
|
||||
# Run with Ollama [ Ollama Run ]
|
||||
|
||||
## Overview
|
||||
|
||||
Ollama is a powerful tool that allows you to run machine learning models effortlessly. This guide will help you download, install, and run your own GGUF models in just a few minutes.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Download and Install Ollama](#download-and-install-ollama)
|
||||
- [Steps to Run GGUF Models](#steps-to-run-gguf-models)
|
||||
- [1. Create the Model File](#1-create-the-model-file)
|
||||
- [2. Add the Template Command](#2-add-the-template-command)
|
||||
- [3. Create and Patch the Model](#3-create-and-patch-the-model)
|
||||
- [Running the Model](#running-the-model)
|
||||
- [Sample Usage](#sample-usage)
|
||||
|
||||
## Download and Install Ollama🦙
|
||||
|
||||
To get started, download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your Windows or Mac system.
|
||||
|
||||
## Steps to Run GGUF Models
|
||||
|
||||
### 1. Create the Model File
|
||||
First, create a model file and name it appropriately. For example, you can name your model file `metallama`.
|
||||
|
||||
### 2. Add the Template Command
|
||||
In your model file, include a `FROM` line that specifies the base model file you want to use. For instance:
|
||||
|
||||
```bash
|
||||
FROM Llama-3.2-1B.F16.gguf
|
||||
```
|
||||
|
||||
Ensure that the model file is in the same directory as your script.
|
||||
|
||||
### 3. Create and Patch the Model
|
||||
Open your terminal and run the following command to create and patch your model:
|
||||
|
||||
```bash
|
||||
ollama create metallama -f ./metallama
|
||||
```
|
||||
|
||||
Once the process is successful, you will see a confirmation message.
|
||||
|
||||
To verify that the model was created successfully, you can list all models with:
|
||||
|
||||
```bash
|
||||
ollama list
|
||||
```
|
||||
|
||||
Make sure that `metallama` appears in the list of models.
|
||||
|
||||
---
|
||||
|
||||
## Running the Model
|
||||
|
||||
To run your newly created model, use the following command in your terminal:
|
||||
|
||||
```bash
|
||||
ollama run metallama
|
||||
```
|
||||
|
||||
### Sample Usage / Test
|
||||
|
||||
In the command prompt, you can execute:
|
||||
|
||||
```bash
|
||||
D:\>ollama run metallama
|
||||
```
|
||||
|
||||
You can interact with the model like this:
|
||||
|
||||
```plaintext
|
||||
>>> write a mini passage about space x
|
||||
Space X, the private aerospace company founded by Elon Musk, is revolutionizing the field of space exploration.
|
||||
With its ambitious goals to make humanity a multi-planetary species and establish a sustainable human presence in
|
||||
the cosmos, Space X has become a leading player in the industry. The company's spacecraft, like the Falcon 9, have
|
||||
demonstrated remarkable capabilities, allowing for the transport of crews and cargo into space with unprecedented
|
||||
efficiency. As technology continues to advance, the possibility of establishing permanent colonies on Mars becomes
|
||||
increasingly feasible, thanks in part to the success of reusable rockets that can launch multiple times without
|
||||
sustaining significant damage. The journey towards becoming a multi-planetary species is underway, and Space X
|
||||
plays a pivotal role in pushing the boundaries of human exploration and settlement.
|
||||
```
|
||||
---
|
||||
### **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.
|
||||
---
|
||||
## Conclusion
|
||||
|
||||
With these simple steps, you can easily download, install, and run your own models using Ollama. Whether you're exploring the capabilities of Llama or building your own custom models, Ollama makes it accessible and efficient.
|
||||
|
||||
|
||||
- This README provides clear instructions and structured information to help users navigate the process of using Ollama effectively. Adjust any sections as needed based on your specific requirements or additional details you may want to include.
|
||||
|
||||
---
|
||||
3
config.json
Normal file
3
config.json
Normal file
@@ -0,0 +1,3 @@
|
||||
{
|
||||
"model_type": "qwen2"
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
Reference in New Issue
Block a user