Field-adaptive-query-generator-gguf/README.md

---
license: apache-2.0
base_model:
- unsloth/gemma-3-4b-it-unsloth-bnb-4bit
- mudasir13cs/Field-adaptive-query-generator
tags:
- text-generation
- gemma
- presentation-templates
- information-retrieval
- field-adaptive
- query-generation
- search-queries
datasets:
- cyberagent/crello
language:
- en
---

# Field-Adaptive Query Generator

A fine-tuned text generation model for generating diverse and relevant search queries from presentation template metadata. This model uses LoRA adapters to efficiently fine-tune Google Gemma-3-4B-IT for generating search queries as part of the Field-Adaptive Dense Retrieval framework.

## Model Description

This model generates 8 different search queries from presentation template metadata including titles, descriptions, industries, categories, and tags. It serves as a key component in the Field-Adaptive Dense Retrieval system for structured documents.

**Base Model:** `unsloth/gemma-3-4b-it-unsloth-bnb-4bit`  
**Model Type:** Causal Language Model with LoRA  
**Language:** English  
**License:** Apache 2.0

## Usage

### With Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "mudasir13cs/Field-adaptive-query-generator"
)
tokenizer = AutoTokenizer.from_pretrained(
    "mudasir13cs/Field-adaptive-query-generator"
)

# Format prompt using Gemma chat template
prompt = """<start_of_turn>user
Generate 8 different search queries that users might use to find this presentation template:
    Title: Modern Business Presentation
    Description: This modern business presentation template features a minimalist design...
    Industries: Business, Marketing
    Categories: Corporate, Professional
    Tags: Modern, Clean, Professional
<end_of_turn>
<start_of_turn>model
"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=512, temperature=0.7, do_sample=True)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```

### With llama.cpp

```bash
# Download the GGUF model
huggingface-cli download mudasir13cs/Field-adaptive-query-generator-gguf \
    query-generator-q4_k_m.gguf --local-dir . --local-dir-use-symlinks False

# Run inference
./llama-cli -m query-generator-q4_k_m.gguf \
    -p "<start_of_turn>user
Generate 8 different search queries that users might use to find this presentation template:
    Title: Modern Business Presentation
    Description: This modern business presentation template features a minimalist design...
    Industries: Business, Marketing
    Categories: Corporate, Professional
    Tags: Modern, Clean, Professional
<end_of_turn>
<start_of_turn>model
"
```

### With Ollama

```bash
# Import model to Ollama
ollama create field-adaptive-query-generator -f Modelfile

# Run inference
ollama run field-adaptive-query-generator "<start_of_turn>user
Generate 8 different search queries that users might use to find this presentation template:
    Title: Modern Business Presentation
    Description: This modern business presentation template features a minimalist design...
    Industries: Business, Marketing
    Categories: Corporate, Professional
    Tags: Modern, Clean, Professional
<end_of_turn>
<start_of_turn>model
"
```

## Expected Output Format

The model generates exactly 8 queries, one per line, with no numbering or bullets:

```
business presentation template
modern corporate slides
professional marketing presentation
blue gradient business template
minimalist corporate design
marketing pitch template
geometric business slides
clean professional presentation
```

## Prompt Format

Always use the Gemma chat template format:

```
<start_of_turn>user
Generate 8 different search queries that users might use to find this presentation template:
    Title: [Template Title]
    Description: [Template Description]
    Industries: [Industry1, Industry2]
    Categories: [Category1, Category2]
    Tags: [Tag1, Tag2, Tag3]

    Include a mix of:
    - Short queries (2-3 words)
    - Medium queries (4-6 words)
    - Natural language queries
    - Industry-specific queries
    - Use-case based queries
    - Style-based queries

    Format: Return exactly 8 queries, one per line, no numbering or bullets.
<end_of_turn>
<start_of_turn>model
```

## Model Details

- **Architecture:** Google Gemma-3-4B-IT with LoRA adapters
- **Training:** Parameter-Efficient Fine-Tuning (PEFT) with LoRA
- **LoRA Rank:** 16
- **LoRA Alpha:** 32
- **Training Epochs:** 3
- **Learning Rate:** 2e-4
- **Batch Size:** 4

## Evaluation

- **BLEU Score:** ~0.75
- **ROUGE Score:** ~0.80
- **Performance:** Optimized for query generation quality in structured document retrieval

## Citation

### Paper

```bibtex
@article{field_adaptive_dense_retrieval,
  title={Field-Adaptive Dense Retrieval of Structured Documents},
  author={Mudasir Syed},
  journal={DBPIA},
  year={2024},
  url={https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE12352544}
}
```

### Model

```bibtex
@misc{field_adaptive_query_generator,
  title={Field-adaptive-query-generator for Presentation Template Query Generation},
  author={Mudasir Syed},
  year={2024},
  howpublished={Hugging Face},
  url={https://huggingface.co/mudasir13cs/Field-adaptive-query-generator}
}
```

### Base Model

```bibtex
@misc{gemma_3_4b_it,
  title={Gemma: Open Models Based on Gemini Research and Technology},
  author={Gemma Team and others},
  year={2024},
  howpublished={Hugging Face},
  url={https://huggingface.co/google/gemma-3-4b-it}
}
```

## Related Models

- [Field-Adaptive Description Generator](https://huggingface.co/mudasir13cs/Field-adaptive-description-generator) - Generates descriptions from template metadata

## Author

Mudasir Syed (mudasir13cs)
- GitHub: https://github.com/mudasir13cs
- HuggingFace: https://huggingface.co/mudasir13cs
- LinkedIn: https://pk.linkedin.com/in/mudasir-sayed