gemma-3-270m-lora-finetuned/README.md

base_model, library_name, pipeline_tag, license, tags

base_model	library_name	pipeline_tag	license	tags
unsloth/gemma-3-270m-it	transformers	text-generation	gemma	gemma3 lora sft unsloth gguf

gemma-3-270m-lora-finetuned

A fine-tuned Gemma 3 270M model with LoRA adapters, trained on instruction-following data.

Model Details

• Base Model: unsloth/gemma-3-270m-it
• Model Type: LoRA fine-tuned (merged)
• Language: English (instruction-tuned)
• License: Gemma License

Available Formats

Format	File	Use Case
Safetensors	model.safetensors	Transformers, vLLM
GGUF Q8_0	gguf/gemma-3-270m-Q8_0.gguf	llama.cpp, Ollama
GGUF BF16	gguf/gemma-3-270m-auto.gguf	llama.cpp (full precision)

Uses

Direct Use

• Instruction following
• Conversational AI
• Question answering
• Text generation

Out-of-Scope Use

• Medical or legal advice
• Generating harmful content
• Production use without evaluation

Training Details

Training Data

• Dataset: mlabonne/FineTome-100k (full dataset)
• Samples: ~100,000
• Data Processing: Standardized format with Gemma 3 chat template

Training Hyperparameters

Parameter	Value
Precision	bfloat16
Max Sequence Length	1024
Per Device Batch Size	2
Gradient Accumulation Steps	4
Effective Batch Size	8
Max Steps	12,500
Learning Rate	2e-4
Warmup Steps	2
Optimizer	adamw_8bit
Weight Decay	0.001
LR Scheduler	linear
Seed	3407

LoRA Configuration

Parameter	Value
LoRA Rank (r)	128
LoRA Alpha	128
LoRA Dropout	0
Bias	none
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Training Stats

• Training Time: ~98 minutes (1h 38m)
• Peak VRAM Usage: 12.4 GB (77.9% of 16 GB)

Compute Infrastructure

• Hardware: NVIDIA RTX 5070 Ti (16 GB VRAM)
• Framework: Unsloth, Transformers, TRL

How to Get Started

Using Transformers (Safetensors)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("chanifrusydi/gemma-3-270m-lora-finetuned")
tokenizer = AutoTokenizer.from_pretrained("chanifrusydi/gemma-3-270m-lora-finetuned")

messages = [{"role": "user", "content": "Hello, how are you?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
)

output = model.generate(inputs, max_new_tokens=64, temperature=0.7)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Using GGUF (llama.cpp / Ollama)

# Download GGUF
wget https://huggingface.co/chanifrusydi/gemma-3-270m-lora-finetuned/resolve/main/gguf/gemma-3-270m-Q8_0.gguf

# Run with llama.cpp
./llama-cli -m gemma-3-270m-Q8_0.gguf -p "Hello, how are you?"

# Or import to Ollama
ollama create gemma3-finetuned -f Modelfile

Citation

@misc{gemma-3-270m-lora-finetuned,
  author = {chanifrusydi},
  title = {gemma-3-270m-lora-finetuned},
  publisher = {HuggingFace},
  year = {2025}
}

Acknowledgments

• Unsloth for the fast fine-tuning framework
• Google for the Gemma 3 base model
• mlabonne for the FineTome-100k dataset