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 |
|
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}
}