Instructions to use Cathaltwo/gemma3_bilingual_cpt_fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cathaltwo/gemma3_bilingual_cpt_fast with Transformers:

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

pipe = pipeline("text-generation", model="Cathaltwo/gemma3_bilingual_cpt_fast")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cathaltwo/gemma3_bilingual_cpt_fast")
model = AutoModelForCausalLM.from_pretrained("Cathaltwo/gemma3_bilingual_cpt_fast")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Cathaltwo/gemma3_bilingual_cpt_fast with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cathaltwo/gemma3_bilingual_cpt_fast"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cathaltwo/gemma3_bilingual_cpt_fast",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Cathaltwo/gemma3_bilingual_cpt_fast

SGLang

How to use Cathaltwo/gemma3_bilingual_cpt_fast 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 "Cathaltwo/gemma3_bilingual_cpt_fast" \
    --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": "Cathaltwo/gemma3_bilingual_cpt_fast",
		"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 "Cathaltwo/gemma3_bilingual_cpt_fast" \
        --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": "Cathaltwo/gemma3_bilingual_cpt_fast",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Cathaltwo/gemma3_bilingual_cpt_fast with Docker Model Runner:
```
docker model run hf.co/Cathaltwo/gemma3_bilingual_cpt_fast
```

gemma3_bilingual_cpt_fast

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 3.0043
Model Preparation Time: 0.0337

Model description

Simple Continued pretraining of gemma-3-270m for Irish (80%) and English (20%) as a test for generating better Irish understanding. Heavily influenced by the work done by reliableai

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0004
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
training_steps: 2000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time
3.8479	0.1414	400	3.7987	0.0337
3.6146	0.2828	800	3.5188	0.0337
3.3704	0.4242	1200	3.3405	0.0337
3.2304	0.5655	1600	3.2068	0.0337
3.1446	0.7069	2000	3.1178	0.0337

Framework versions

Transformers 4.56.1
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.22.0

Downloads last month: 2

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for Cathaltwo/gemma3_bilingual_cpt_fast

Base model

google/gemma-3-270m

Finetuned

(140)

this model

Cathaltwo
/

gemma3_bilingual_cpt_fast