---
license: apache-2.0
language:
- tr
- en
base_model: SykoSLM/SykoLLM-V5.9-Mini
pipeline_tag: text-generation
library_name: transformers
tags:
- nlp
- code
- phi3
- depth-up-scaling
- untrained
---

# SykoLLM-V6.0-Test

## Model Overview
**SykoLLM-V6.0-Test** is an up-scaled and structurally expanded version of the previous SykoLLM models. Developed by **SykoSLM**, this model is currently in the experimental/testing phase. 

The primary objective of this release is to provide a structurally larger foundation model by expanding both the depth (number of layers) and the width (intermediate size / MLP capacity) of the previous architecture, without losing the pre-trained knowledge.

## Architectural Expansion (Up-Scaling)
In order to overcome the "Knowledge Interference" (capacity bottleneck) observed in previous iterations, significant architectural changes have been applied to this model:

* **Depth Up-Scaling (DUS):** The number of hidden layers has been increased to **24**. This was achieved by carefully duplicating and mapping the existing layers to preserve the logical and syntactic capabilities of the model.
* **Width Expansion (MLP Scaling):** The `intermediate_size` has been expanded to **3072**. To prevent catastrophic forgetting, the newly added weights in the feed-forward networks were initialized with exact zero (`0.0`). This ensures that the newly added parameters act as identity functions during the initial forward pass.

## ⚠️ Important Notice: Status of the Model
**This model is currently UNTRAINED on the newly added parameters.** 

It has been expanded solely to save pre-training time and preserve existing knowledge. While the model retains the capabilities of its predecessor, the newly added parameters (~100M+ new parameters) are currently dormant (zeroed out). 

To fully utilize the expanded capacity and activate the new parameters, **fine-tuning is required**. If used in its current state, the model will function similarly to the previous smaller version, as the new structural capacity has not yet been fine-tuned on new or existing datasets.

## Why This Approach?
Training a Large Language Model from scratch requires immense computational resources and time. By utilizing Net2Net (Knowledge Distillation) principles:
1. We preserve the billions of tokens worth of knowledge already embedded in the model.
2. We provide the model with a much larger "encyclopedic" memory (MLP expansion) to prevent data overlapping and hallucination.
3. We drastically reduce the time required to achieve a higher parameter count.

## Usage
You can load the model using the `transformers` library, but please keep in mind that it requires further fine-tuning for optimal performance.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SykoSLM/SykoLLM-V6.0-Test"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
```

---

Developed by SykoSLM