---
library_name: transformers
tags: [qlora, peft, fine-tuning, javascript, causal-lm]
---

# Model Card for gemma-js-instruct-finetune

## Model Details

### Model Description

This is the model card for `gemma-js-instruct-finetune`, a fine-tuned version of the `gemma-2b-it` model. This fine-tuned model was trained to improve the performance of generating long-form, structured responses to JavaScript-related instructional tasks. The fine-tuning process used the QLoRA (Quantized Low-Rank Adaptation) method, enabling efficient parameter tuning on limited hardware resources.

- **Developed by:** Arnav Jain and collaborators
- **Shared by:** [Arnav Jain](https://huggingface.co/arnavj007)
- **Model type:** Decoder-only causal language model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** [gemma-2b-it](https://huggingface.co/google/gemma-2b-it)

### Model Sources

- **Repository:** [gemma-js-instruct-finetune](https://huggingface.co/arnavj007/gemma-js-instruct-finetune)
- **Dataset:** [Evol-Instruct-JS-Code-500-v1](https://huggingface.co/datasets/pyto-p/Evol-Instruct-JS-Code-500-v1)
- **Demo:** [Weights & Biases Run](https://wandb.ai/arnavj007-24/huggingface/runs/718nwcab)

## Uses

### Direct Use

The model can be directly used for generating solutions to JavaScript programming tasks, creating instructional code snippets, and answering technical questions related to JavaScript programming.

### Downstream Use

This model can be further fine-tuned for specific programming domains, other languages, or instructional content generation tasks.

### Out-of-Scope Use

This model is not suitable for:
- Non-technical, general-purpose text generation
- Applications requiring real-time interaction with external systems
- Generating solutions for non-JavaScript programming tasks without additional fine-tuning

## Bias, Risks, and Limitations

### Recommendations

- Users should validate generated code for correctness and security.
- Be cautious of potential biases or inaccuracies in the dataset that could propagate into model outputs.
- Avoid using the model for sensitive or critical applications without thorough testing.

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arnavj007/gemma-js-instruct-finetune")
model = AutoModelForCausalLM.from_pretrained("arnavj007/gemma-js-instruct-finetune")

def get_completion(query: str):
    prompt = f"<start_of_turn>user {query}<end_of_turn>\n<start_of_turn>model"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_new_tokens=1000)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

response = get_completion("Create a function in JavaScript to calculate the factorial of a number.")
print(response)
```

## Training Details

### Training Data

The training dataset consisted of 500 JavaScript instructions paired with relevant outputs. These instructions focused on tasks like code snippets, algorithm implementations, and error-handling scenarios.

Dataset: [Evol-Instruct-JS-Code-500-v1](https://huggingface.co/datasets/pyto-p/Evol-Instruct-JS-Code-500-v1)

### Training Procedure

#### Preprocessing

- Instructions and outputs were formatted using a standardized prompt-response template.
- Data was tokenized using the Hugging Face tokenizer for `gemma-2b-it`.

#### Training Hyperparameters

- **Training regime:** QLoRA (Quantized Low-Rank Adaptation)
- **Batch size:** 1 per device
- **Gradient accumulation steps:** 4
- **Learning rate:** 2e-4
- **Training steps:** 100
- **Optimizer:** Paged AdamW (8-bit)

### Speeds, Sizes, Times

- Training runtime: ~1435 seconds
- Trainable parameters: 3% of the model (~78M)

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

The test dataset consisted of 100 JavaScript instructions held out from the training set.

#### Metrics

- Quality of generated code snippets
- Ability to handle complex prompts with multiple sub-tasks

### Results

The fine-tuned model demonstrated significant improvement in handling long prompts and generating structured code. It provided complete solutions for tasks like API creation with advanced features (e.g., caching, error handling).

#### Summary

Fine-tuning with QLoRA enabled robust performance improvements, making the model capable of generating detailed instructional responses.

## Environmental Impact

- **Hardware Type:** NVIDIA Tesla T4 GPU (free-tier Colab)
- **Hours used:** ~0.4 hours
- **Carbon Emitted:** Minimal (estimated using [ML Impact Calculator](https://mlco2.github.io/impact#compute))

## Technical Specifications

### Model Architecture and Objective

The model uses a decoder-only architecture optimized for causal language modeling tasks.

### Compute Infrastructure

- **Hardware:** NVIDIA Tesla T4
- **Software:**
  - Transformers: 4.38.2
  - PEFT: 0.8.2
  - Accelerate: 0.27.1
  - BitsAndBytes: 0.42.0

## Citation

**BibTeX:**
```bibtex
@misc{Jain2024gemmajs,
  author = {Arnav Jain and collaborators},
  title = {gemma-js-instruct-finetune},
  year = {2024},
  howpublished = {\url{https://huggingface.co/arnavj007/gemma-js-instruct-finetune}}
}
```

## More Information

For questions or feedback, contact [Arnav Jain](https://huggingface.co/arnavj007).