File size: 8,563 Bytes
d9ddc1d a0c8b44 d3aa7b0 d9ddc1d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 |
---
license: creativeml-openrail-m
datasets:
- GAIR/o1-journey
language:
- en
base_model:
- prithivMLmods/Acrux-500M-o1-Journey
library_name: transformers
pipeline_tag: text-generation
tags:
- Qwen2.5
- 500M
- Ollama
- Llama-cpp
- text-generation-inference
- o1-Journey
---
### Acrux-500M-o1-Journey-GGUF Model Files
The **Acrux-500M-o1-Journey** is a lightweight, instruction-tuned language model fine-tuned from the **Qwen2.5-0.5B-Instruct** base model. With a size of 500 million parameters, it is designed for **cost-effective deployment** and **fast text generation** while maintaining quality performance for instruction-following tasks.
| **File Name** | **Size** | **Description** | **Upload Status** |
|-----------------------------------------|----------------|-----------------------------------------------|--------------------|
| `.gitattributes` | 2.42 kB | Specifies file tracking rules (e.g., LFS). | Uploaded |
| `Modelfile` | 1.69 kB | Metadata or additional information file. | Uploaded |
| `README.md` | 158 Bytes | Basic project description or instructions. | Updated |
| `acrux-500m-o1-journey-f16.gguf` | 994 MB | Base FP16 model file in GGUF format. | Uploaded (LFS) |
| `acrux-500m-o1-journey-q2_k.gguf` | 339 MB | Quantized model (Q2_K) for efficient usage. | Uploaded (LFS) |
| `acrux-500m-o1-journey-q3_k_l.gguf` | 369 MB | Quantized model (Q3_K_L). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q3_k_m.gguf` | 355 MB | Quantized model (Q3_K_M). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q3_k_s.gguf` | 338 MB | Quantized model (Q3_K_S). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q4_0.gguf` | 352 MB | Quantized model (Q4_0). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q4_k_m.gguf` | 398 MB | Quantized model (Q4_K_M). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q4_k_s.gguf` | 385 MB | Quantized model (Q4_K_S). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q5_0.gguf` | 397 MB | Quantized model (Q5_0). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q5_k_m.gguf` | 420 MB | Quantized model (Q5_K_M). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q5_k_s.gguf` | 413 MB | Quantized model (Q5_K_S). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q6_k.gguf` | 506 MB | Quantized model (Q6_K). | Uploaded (LFS) |
| `acrux-500m-o1-journey-q8_0.gguf` | 531 MB | Quantized model (Q8_0). | Uploaded (LFS) |
| `config.json` | 29 Bytes | Basic configuration file for the model. | Uploaded |
---
### **Key Features:**
1. **Compact Size with Efficient Performance:**
The smaller parameter count (500M) ensures faster inference and reduced hardware requirements.
2. **Instruction Optimization:**
Fine-tuned to follow prompts effectively, making it suitable for interactive applications and prompt-based tasks.
3. **Domain-Specific Training:**
Trained on the **GAIR/o1-journey** dataset, providing tailored capabilities for specific use cases.
---
### **Training Details:**
- **Base Model:** [Qwen2.5-0.5B-Instruct](#)
- **Dataset Used for Fine-Tuning:** [GAIR/o1-journey](#)
- A compact dataset focusing on instruction-driven generation with 1.42k samples.
---
### **Capabilities:**
1. **Instruction Following:**
- Generates accurate and coherent responses to user instructions.
- Handles summarization, question-answering, and conversational tasks.
2. **Fast Inference:**
- Ideal for real-time applications due to reduced latency from its smaller size.
3. **Interactive AI Development:**
- Suitable for chatbots, virtual assistants, and instructional interfaces.
---
### **Usage Instructions:**
1. **Setup:**
Download all model files, ensuring compatibility with the Hugging Face Transformers library.
2. **Loading the Model:**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Acrux-500M-o1-Journey"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
```
3. **Sample Generate Text:**
```python
input_text = "Explain the concept of machine learning in simple terms."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
4. **Optimize Generation:**
Adjust parameters in `generation_config.json` for better control of output, such as:
- `temperature` for randomness.
- `top_p` for sampling diversity.
- `max_length` for output size.
---
# 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.
```
---
## 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.
--- |