---
license: apache-2.0
datasets:
- ciol-research/global-festivals-wiki
- WorkWithData/cities
- Yelp/yelp_review_full
- dair-ai/emotion
language:
- en
- kn
- hi
- es
- zh
- te
- de
- ko
- sq
- fr
- id
- pl
- it
- vi
- tr
- ru
- he
- ar
- fa
- bn
- th
- ja
base_model:
- google/gemma-3-1b-it
pipeline_tag: text-generation
---
# 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)

---

## 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

```py
# 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:
   ```bash
   @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}}
    }
  ```