Gemma-3-270m-Buddha-QA

Gemma-3-270m Buddha-QA (LoRA 4-bit)

Model Details

• Developed by: sweatSmile
• Base model: google/gemma-3-270m
• Fine-tuning method: LoRA (Low-Rank Adaptation)
• Quantization: 4-bit (nf4) with double quantization
• Task type: Question Answering (QA)
• Language: English
• License: Apache-2.0

This model was fine-tuned on a QA dataset about Buddhist teachings, designed for lightweight question-answering tasks.

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Model Sources

• Repository: Hugging Face Model Repo
• Dataset: sweatSmile/buddha-taught-qa

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Uses

Direct Use

• Educational QA about Buddhist texts.
• Lightweight inference on constrained hardware (4-bit quantization).

Downstream Use

• Can be adapted to other domain-specific QA tasks with further LoRA fine-tuning.

Out-of-Scope Use

• Not suitable for open-domain QA beyond its training dataset.
• Should not be used for sensitive or factual decision-making without verification.

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Bias, Risks, and Limitations

• Dataset is small (699 QA pairs), so generalization is limited.
• Answers are narrow and domain-specific (Buddhist context).
• May generate incomplete or repetitive answers outside training distribution.

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Training Details

Training Data

• Dataset: sweatSmile/buddha-taught-qa (699 QA pairs).
• Preprocessed into {"prompt": ..., "completion": ...} format.

Training Procedure

• Frameworks: PEFT + TRL + Transformers
• Precision: 4-bit quantization (nf4, double quantization, compute dtype = bf16 if supported)
• LoRA Config:
  • r = 8
  • lora_alpha = 16
  • lora_dropout = 0.1
  • Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Training Hyperparameters:
  • effective_batch_size = 6
  • gradient_accumulation_steps = 3
  • num_train_epochs = 8
  • learning_rate = 2e-4
  • lr_scheduler_type = cosine
  • warmup_ratio = 0.05
  • weight_decay = 0.01
  • max_grad_norm = 1.0
  • fp16 = True
  • max_seq_length = 64
  • save_total_limit = 2
  • Logging & checkpoint every 15 steps

Results

• Global steps: 256
• Final training loss: ~1.81
• Train runtime: ~373s
• Train samples/sec: ~12
• Train steps/sec: ~0.69

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Evaluation

Qualitative Examples

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How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "sweatSmile/Gemma-3-270m-Buddha-QA"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

inputs = tokenizer("Who is referred to as the Fully-Enlightened One in the text?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Environmental Impact

• Hardware: Single GPU (Colab T4)
• Precision: 4-bit + mixed precision
• Training duration: ~20 minutes
• Carbon footprint: negligible compared to large-scale LLMs.

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Citation

If you use this model, please cite:

@misc{gemma-buddha-qa-2025,
  title = {Gemma-3-270m Buddha-QA (LoRA 4-bit)},
  author = {sweatSmile},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/sweatSmile/Gemma-3-270m-Buddha-QA}}
}

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Contact

• Author: sweatSmile
• Hugging Face: profile