README.md

---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
- lora
- sft
- transformers
- trl
---

# Model Card for tinyllama-structured-output-lora

This model is a LoRA fine-tuned version of TinyLlama designed to generate structured JSON outputs from natural language instructions.

---

## Model Details

### Model Description

This model was fine-tuned using QLoRA on the Databricks Dolly 15K dataset transformed into a structured instruction-to-JSON generation task. The goal of the project is to improve schema consistency and structured response formatting in LLM outputs.

The model learns to generate responses in a predefined JSON structure instead of plain conversational text.

- **Developed by:** ABI
- **Funded by [optional]:** Self-funded
- **Shared by [optional]:** ABI
- **Model type:** Causal Language Model with LoRA adapters
- **Language(s) (NLP):** English
- **License:** Apache 2.0 (inherits base model license compatibility)
- **Finetuned from model [optional]:** TinyLlama/TinyLlama-1.1B-Chat-v1.0

---

## Model Sources [optional]

- **Repository:** https://huggingface.co/your-username/tinyllama-structured-output-lora
- **Paper [optional]:** https://arxiv.org/abs/2106.09685 (LoRA Paper)
- **Demo [optional]:** Not available

---

## Uses

This model is intended for experimentation and educational purposes related to:
- structured output generation
- instruction fine-tuning
- LoRA adaptation
- JSON schema enforcement

---

### Direct Use

The model can be used for:
- converting instructions into structured JSON responses
- schema-constrained text generation
- learning and experimentation with QLoRA pipelines
- educational demonstrations of instruction tuning

Example task:
```json
{
  "question": "Explain recursion",
  "context_summary": "",
  "answer": "Recursion is a programming concept...",
  "category": "education",
  "difficulty": "easy"
}
```

---

### Downstream Use [optional]

Possible downstream applications include:
- structured chatbot systems
- API response generation
- educational assistants
- JSON formatting pipelines
- schema-aware LLM systems

---

## Training Details

### Training Dataset

- Databricks Dolly 15K
- Dataset transformed into structured JSON generation format

### Training Procedure

The model was fine-tuned using:
- QLoRA
- 4-bit quantization
- PEFT (Parameter Efficient Fine-Tuning)

### Hardware

- Google Colab T4 GPU (16GB VRAM)

### Main Libraries Used

- transformers
- peft
- trl
- datasets
- bitsandbytes

---

## Limitations

- Small model size limits reasoning capability
- May produce incomplete JSON occasionally
- Responses may repeat under long generation settings
- Optimized for structure rather than factual accuracy

---

## Example Inference Code

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import torch

model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4"
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

base_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto"
)

model = PeftModel.from_pretrained(
    base_model,
    "your-username/tinyllama-structured-output-lora"
)

prompt = """
### Instruction:
Explain recursion

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=120,
    temperature=0.1,
    do_sample=False
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## Citation

```bibtex
@misc{tinyllama_structured_output_lora,
  title={TinyLlama Structured Output LoRA},
  author={ABI},
  year={2026},
  publisher={Hugging Face}
}
```