Instructions to use dwikitheduck/gemma-2-2b-id-inst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- Unsloth Studio new
How to use dwikitheduck/gemma-2-2b-id-inst with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for dwikitheduck/gemma-2-2b-id-inst to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for dwikitheduck/gemma-2-2b-id-inst to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dwikitheduck/gemma-2-2b-id-inst to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="dwikitheduck/gemma-2-2b-id-inst", max_seq_length=2048, )
Experiment 1 SFT ALPACA INDO
dataset: 9 millions token indo alpaca dataset
max_seq_length = 8192, dataset_num_proc = 2, packing = False, args = TrainingArguments( per_device_train_batch_size = 1, gradient_accumulation_steps = 8, warmup_steps = 5, num_train_epochs = 1, learning_rate = 5e-5, fp16 = not is_bfloat16_supported(), bf16 = is_bfloat16_supported(), logging_steps = 1, optim = "adamw_8bit", weight_decay = 0.01, lr_scheduler_type = "linear", seed = 3407,
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