Instructions to use canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1")
model = AutoModelForCausalLM.from_pretrained("canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1

SGLang

How to use canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1 with Docker Model Runner:
```
docker model run hf.co/canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1
```

Model Card: Gemma3-1B Turkish CPT (2nd Epoch – Stage 1, 0K–50K Subset, 1 Epoch)

Overview

This model is the 2nd-epoch Stage 1 Turkish Continued Pretraining (CPT) variant of Gemma-3-1B.

Unlike the initial epoch stages that started from google/gemma-3-1b-pt,
this model is initialized from the end of epoch-1 checkpoint:

canbingol/gemma3_1B_base-tr-cpt-1epoch_stage4

Stage 1 of the second epoch continues domain adaptation by revisiting the dataset from the beginning (shard-based sequential pass).

The model was trained for 1 epoch on samples 0 to 50,000 of the Turkish web corpus.

Conceptually:

Epoch-1 was completed via sequential shards (0–200K).
This release begins epoch-2, again using sequential shards, starting from 0–50K.

Training Lineage

Stage 0: google/gemma-3-1b-pt
Epoch-1 Stage 1: Samples 0–50,000 (1 epoch)
Epoch-1 Stage 2: Samples 50,000–100,000 (1 epoch)
Epoch-1 Stage 3: Samples 100,000–150,000 (1 epoch)
Epoch-1 Stage 4: Samples 150,000–200,000 (1 epoch, end of epoch-1)
Epoch-2 Stage 1 (this release): Samples 0–50,000 (1 epoch)

This represents sequential CPT across disjoint data shards, repeated for a second epoch.

Training Setup

Dataset: canbingol/vngrs-web-corpus-200k
Subset Used: Samples 0–50,000
Initialization: canbingol/gemma3_1B_base-tr-cpt-1epoch_stage4
Training Objective: Continued Pretraining
Epochs: 1 (for this shard)
Data Regime: Plain text
Token Count (this stage): ~21.6M tokens
Cumulative Token Exposure:
- After epoch-1: ~86.1M tokens (approximate)
- After this stage (epoch-2 stage1): ~107.7M tokens (approximate)

Notes on cumulative exposure:

This stage re-exposes the model to the 0–50K shard, but with the parameters already adapted through the full first epoch.
Therefore it is best understood as continued optimization / refinement rather than “new data” exposure.

Usage Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "canbingol/gemma3_1B_base-tr-cpt-2nd_epoch_stage1"

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)