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Model: canbingol/gemma3_1B_base-tr-cpt-1epoch_stage4
Source: Original Platform

.gitattributes

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README.md

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+---
+library_name: transformers
+tags:
+- trl
+- cpt
+datasets:
+- canbingol/vngrs-web-corpus-200k
+language:
+- tr
+- en
+base_model:
+- canbingol/gemma3_1B_base-tr-cpt-1epoch_stage3
+new_version: canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1
+---
+
+# Model Card: Gemma3-1B Turkish CPT (150K–200K Subset, 1 Epoch – Stage 4)
+
+## Overview
+
+This model is the **Stage 4** Turkish Continued Pretraining (CPT) variant of Gemma-3-1B.
+
+Unlike Stage 1, which was initialized from `google/gemma-3-1b-pt`,  
+this model was initialized from:
+
+- `canbingol/gemma3_1B_base-tr-cpt-1epoch_stage3`
+
+Stage 4 continues domain adaptation by exposing the model to **new data** rather than repeating the same subset.
+
+The model was trained for **1 epoch** on samples **150,000 to 200,000** of the Turkish web corpus.
+
+Importantly, this model is a direct continuation of Stage 3.  
+Therefore, cumulatively it has been trained on samples **0–200,000** of the corpus (Stage 1: 0–50K, Stage 2: 50K–100K, Stage 3: 100K–150K, Stage 4: 150K–200K).
+
+This stage corresponds to the **end of the 1-epoch pass over the full 200K-sample dataset** (i.e., completion of the first full epoch via sequential shards).
+
+---
+
+## Training Lineage
+
+- Stage 0: `google/gemma-3-1b-pt`
+- Stage 1: Samples 0–50,000 (1 epoch)
+- Stage 2: Samples 50,000–100,000 (1 epoch)
+- Stage 3: Samples 100,000–150,000 (1 epoch)
+- Stage 4 (this release): Samples 150,000–200,000 (1 epoch, end of epoch-1)
+
+Cumulative data exposure: **0–200,000 samples**
+
+This represents **sequential CPT across disjoint data shards**.
+
+---
+
+## Training Setup
+
+- Dataset: `canbingol/vngrs-web-corpus-200k`
+- Subset Used: Samples 150,000–200,000
+- Initialization: Stage 3 checkpoint
+- Training Objective: Continued Pretraining
+- Epochs: 1
+- Data Regime: Plain text
+- Token Count: **~21.6M tokens**
+- Cumulative Token Exposure (Stage 1 + Stage 2 + Stage 3 + Stage 4): **~86.1M tokens (approximate)**
+
+Notes on cumulative exposure:
+- Although Stage 4 trains only on the 150K–200K shard, it inherits all adaptations learned from previous stages.
+- After this stage, the model has effectively completed exposure to the entire 0–200K dataset range through sequential continuation.
+
+---
+
+## Usage Example
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "canbingol/gemma3_1B_base-tr-cpt-1epoch_stage4"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModelForCausalLM.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = model.to(device)
+
+prompt = "bundan böyle"
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=50,
+    do_sample=True,
+    temperature=0.8,
+    top_p=0.9
+)
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "initializer_range": 0.02,
+  "intermediate_size": 6912,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 1000000,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 10000,
+      "rope_type": "default"
+    }
+  },
+  "sliding_window": 512,
+  "sliding_window_pattern": 6,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.2.0",
+  "use_bidirectional_attention": false,
+  "use_cache": false,
+  "vocab_size": 262144
+}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "5.2.0"
+}

model.safetensors

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tokenizer.json

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "boi_token": "<start_of_image>",
+  "bos_token": "<bos>",
+  "clean_up_tokenization_spaces": false,
+  "eoi_token": "<end_of_image>",
+  "eos_token": "<eos>",
+  "image_token": "<image_soft_token>",
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "model_specific_special_tokens": {
+    "boi_token": "<start_of_image>",
+    "eoi_token": "<end_of_image>",
+    "image_token": "<image_soft_token>"
+  },
+  "pad_token": "<pad>",
+  "sp_model_kwargs": null,
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}