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Model: SykoSLM/SykoLLM-V6.0-Test Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- tr
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- en
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base_model: SykoSLM/SykoLLM-V5.9-Mini
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- nlp
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- code
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- phi3
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- depth-up-scaling
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- untrained
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---
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# SykoLLM-V6.0-Test
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## Model Overview
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**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.
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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.
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## Architectural Expansion (Up-Scaling)
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In order to overcome the "Knowledge Interference" (capacity bottleneck) observed in previous iterations, significant architectural changes have been applied to this model:
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* **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.
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* **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.
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## ⚠️ Important Notice: Status of the Model
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**This model is currently UNTRAINED on the newly added parameters.**
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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).
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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.
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## Why This Approach?
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Training a Large Language Model from scratch requires immense computational resources and time. By utilizing Net2Net (Knowledge Distillation) principles:
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1. We preserve the billions of tokens worth of knowledge already embedded in the model.
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2. We provide the model with a much larger "encyclopedic" memory (MLP expansion) to prevent data overlapping and hallucination.
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3. We drastically reduce the time required to achieve a higher parameter count.
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## Usage
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You can load the model using the `transformers` library, but please keep in mind that it requires further fine-tuning for optimal performance.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "SykoSLM/SykoLLM-V6.0-Test"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
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```
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---
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Developed by SykoSLM
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config.json
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config.json
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{
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"architectures": [
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"Phi3ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"dtype": "float16",
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"embd_pdrop": 0.0,
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"eos_token_id": 0,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"ignore_keys_at_rope_validation": null,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"max_position_embeddings": 1024,
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"model_type": "phi3",
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"num_attention_heads": 8,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"original_max_position_embeddings": 4096,
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"pad_token_id": 4,
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"partial_rotary_factor": 1.0,
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"partial_rotary_factor": 1.0,
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.0.0",
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"use_cache": false,
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"vocab_size": 50000
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"eos_token_id": 0,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 4,
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"transformers_version": "5.0.0",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7dd52b095af4c1ba8b9c625898ced3d9cb8740453ec36495b937cdd9f18bcc90
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size 783731024
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tokenizer.json
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"is_local": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "TokenizersBackend",
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"unk_token": "<|endoftext|>"
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}
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