File size: 1,985 Bytes
885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 7578d05 885c0c4 |
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 |
---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
library_name: peft
---
# Compass Model - Fine-Tuned for Marketing Campaign Generation
## Overview
The **Compass Model** is a specialized language model fine-tuned to generate high-quality, targeted marketing campaigns across various industries and objectives. This model was developed using the **Llama 3.1 405B** base model and fine-tuned on a custom dataset containing **687** carefully curated marketing campaigns. The fine-tuning process leveraged the powerful **Llama 3.1 8B** model using **Unsloth** techniques to optimize performance for marketing-related tasks.
## Model Details
### Model Description
- **Developed by:** [Rafael Montañez]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Fine-tuned Language Model
- **Language(s) (NLP):** English
- **License:** [MIT]
- **Finetuned from model [optional]:** Llama 3.1 405B
## Uses
### Direct Use
The Compass Model is ideal for directly generating marketing content, including social media posts, email marketing material, product launch strategies, and more.
### Downstream Use
This model can be further fine-tuned for specific marketing domains or integrated into AI-driven marketing automation systems.
### Out-of-Scope Use
The model may not perform optimally if used outside of the marketing context or for domains not covered during fine-tuning.
## Bias, Risks, and Limitations
- The model was trained on a limited dataset, potentially leading to suboptimal performance in industries or objectives not represented in the training data.
- Risks include the possibility of generating biased or unbalanced marketing strategies due to biases present in the training data.
### Recommendations
Users should carefully review the model's outputs, especially in sensitive or high-impact marketing scenarios, to mitigate potential biases and ensure the relevance of generated content.
|