PranavKeshav/event-planner-gemma-4bit

Gemma-3-1B Event-Planner (4-bit QLoRA)

Adapter-only repo for a culturally sensitive event-planning assistant fine-tuned via LoRA on google/gemma-3-1b-it.
This adapter (~50 MB) can be applied to the 4-bit base model at inference time, so you don’t need to ship multi-GB merged weights.

Base model: google/gemma-3-4b-it
Fine-tuned with: LoRA r=8, α=32, dropout=0.05, 4-bit NF4 quant.

Intended use

Generates culturally sensitive event plans (weddings, baby-naming, college fests …).
Asks clarifying questions about culture, guest count, budget, dietary needs.

Training data

• dair-ai/emotion (3 k / 0.5 k)
• ciol-research/global-festivals-wiki (9 k / 1 k)
• corbt/all-recipes (15 k / 1.5 k)
• WorkWithData/cities (6 k / 1 k)
• Yelp/yelp_review_full (12 k / 2 k)

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Model Details

• Base model: google/gemma-3-1b-it (4 B parameters, instruction-tuned)
• Quantization: 4-bit NF4 via bitsandbytes
• LoRA config:
  • rank r = 8
  • α = 32
  • dropout = 0.05
  • target modules = ["q_proj","v_proj"]
• Trainable params: ~0.75 M (0.07 % of base)

Fine-tuning data (≈ 75 k examples total)

Domain	Dataset	Train / Val	Why included
Emotion & Tone	dair-ai/emotion	3 k / 0.5 k	Adapt style & follow-up questioning
Cultural Festivals	ciol-research/global-festivals-wiki	9 k / 1 k	Rituals, symbols, dates across cultures
Cuisine & Menus	corbt/all-recipes	15 k / 1.5 k	Authentic recipes for menu planning
Venue / Geodata	WorkWithData/cities	6 k / 1 k	Real cities + coords for location tips
Vendors & Services	Yelp/yelp_review_full	12 k / 2 k	Business vocabulary & recommendation tone

---

Local Usage

# Install runtime dependencies:
pip install accelerate==1.7.0 bitsandbytes==0.45.5 peft==0.15.2 sentencepiece==0.2.0 torch==2.7.0 transformers==4.51.3 trl==0.17.0

# Load the 4-bit base + adapter:
import torch
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    BitsAndBytesConfig,
    pipeline,
)
from peft import PeftModel

# 1. Quant config
bnb_cfg = BitsAndBytesConfig(
    load_in_4bit              = True,
    bnb_4bit_quant_type       = "nf4",
    bnb_4bit_use_double_quant = True,
    bnb_4bit_compute_dtype    = torch.float16,
)

# 2. Tokenizer
BASE = "google/gemma-3-1b-it"
tokenizer = AutoTokenizer.from_pretrained(
    BASE,
    trust_remote_code=True,
    use_auth_token=True,
)

# 3. Base model
base = AutoModelForCausalLM.from_pretrained(
    BASE,
    quantization_config=bnb_cfg,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True,
    use_auth_token=True,
)

# 4. LoRA adapter
ADAPTER = "PranavKeshav/event-planner-gemma-4bit"
model   = PeftModel.from_pretrained(
    base,
    ADAPTER,
    device_map="auto",
    torch_dtype=torch.float16,
    use_auth_token=True,
)
model.eval()

# 5. Pipeline
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto",
    max_new_tokens=150,
    temperature=0.7,
    top_p=0.9,
)

# 6. Test
print(pipe("Plan a Gujarati wedding for 120 guests in Ahmedabad.")[0]["generated_text"])

Model Card & Citation

1. Intended use: Generate culturally sensitive event plans; ask clarifying questions about dates, budgets, dietary needs.
2. Limitations: May hallucinate or miss rare cultural details; verify all critical recommendations.
3. License: Same as google/gemma-3-1b-it (Apache-2.0) + dataset licenses; see individual datasets.
4. Citation:

   @misc{gemma_event_planner_2025,
      title = {Gemma-3-4B Event-Planner LoRA Adapter},
      author = {Keshav, Pranav},
      year = {2025},
      howpublished = {\url{https://huggingface.co/YOUR_USERNAME/event-planner-gemma-4bit}}
    }