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Model: SykoSLM/SykoLLM-V6.0-Test
Source: Original Platform

README.md

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+---
+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