mesolitica/Malaysian-SFT
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How to use Faris-Faiz/Malaysian_Gemma3_270M_F16 with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Faris-Faiz/Malaysian_Gemma3_270M_F16", dtype="auto")How to use Faris-Faiz/Malaysian_Gemma3_270M_F16 with Unsloth Studio:
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 Faris-Faiz/Malaysian_Gemma3_270M_F16 to start chatting
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 Faris-Faiz/Malaysian_Gemma3_270M_F16 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Faris-Faiz/Malaysian_Gemma3_270M_F16 to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="Faris-Faiz/Malaysian_Gemma3_270M_F16",
max_seq_length=2048,
) Model Accuracy shot by_letter category
0 Malaysian_Gemma3_270M_F16 43.143676 0shot True STEM
1 Malaysian_Gemma3_270M_F16 43.861323 0shot True Language
2 Malaysian_Gemma3_270M_F16 44.246892 0shot True Social science
3 Malaysian_Gemma3_270M_F16 43.367714 0shot True Others
4 Malaysian_Gemma3_270M_F16 49.260523 0shot True Humanities
{'Social science': np.int64(6918), 'Language': np.int64(6288), 'Humanities': np.int64(4395), 'Others': np.int64(4169), 'STEM': np.int64(2443)}
Model : Malaysian_Gemma3_270M_F16
Metric : first
Shot : 0shot
average accuracy 44.794118861768474
accuracy for STEM 43.143675808432256
accuracy for Language 43.86132315521628
accuracy for Social science 44.246892165365715
accuracy for Others 43.367714080115135
accuracy for Humanities 49.26052332195677
dataset was fine-tuned on split (split="force_malay")
This gemma3_text model was trained 2x faster with Unsloth and Huggingface's TRL library.