---
library_name: transformers
tags:
- unsloth
- trl
- sft
license: mit
datasets:
- neo4j/text2cypher-2024v1
language:
- en
base_model:
- unsloth/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
---


## Model Card for Llama3.1-8B-Cypher

### Model Details
**Model Description**  
This is the model card for **Llama3.1-8B-Cypher**, a fine-tuned version of Meta’s Llama-3.1-8B, optimized for generating **Cypher queries** from natural language input. The model has been trained using **Unsloth** for efficient fine-tuning and inference.

**Developed by**: Azzedine (GitHub: Azzedde)  
**Funded by [optional]**: N/A  
**Shared by [optional]**: Azzedde  
**Model Type**: Large Language Model (LLM) optimized for Cypher query generation  
**Language(s) (NLP)**: English  
**License**: Apache 2.0  
**Finetuned from model [optional]**: Meta-Llama-3.1-8B-Instruct  

### Model Sources
**Repository**: [Hugging Face](https://huggingface.co/Azzedde/llama3.1-8b-text2cypher)  
**Paper [optional]**: N/A  
**Demo [optional]**: N/A  

### Uses
#### Direct Use
This model is designed for generating **Cypher queries** for **Neo4j databases** based on natural language inputs. It can be used in:
- Database administration
- Knowledge graph construction
- Query automation for structured data retrieval

#### Downstream Use [optional]
- Integrating into **LLM-based database assistants**
- Automating **graph database interactions** in enterprise applications
- Enhancing **semantic search and recommendation systems**

#### Out-of-Scope Use
- General NLP tasks unrelated to graph databases
- Applications requiring strong factual accuracy outside Cypher query generation

### Bias, Risks, and Limitations
- The model may **generate incorrect or suboptimal Cypher queries**, especially for **complex database schemas**.
- The model has not been trained to **validate or optimize queries**, so users should manually **verify generated queries**.
- Limited to **English-language inputs** and **Neo4j graph database use cases**.

### Recommendations
Users should be aware of:
- The importance of **validating model-generated queries** before execution.
- The **potential for biases** in database schema interpretation.
- The need for **fine-tuning on domain-specific datasets** for best performance.

### How to Get Started with the Model
Use the following code to load and use the model:

```python
from unsloth import FastLanguageModel
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Azzedde/llama3.1-8b-text2cypher")
model = FastLanguageModel.from_pretrained("Azzedde/llama3.1-8b-text2cypher")

# Example inference
cypher_prompt = """Below is a database Neo4j schema and a question related to that database. Write a Cypher query to answer the question.

### Schema:
{schema}

### Question:
{question}

### Cypher:
"""
input_text = cypher_prompt.format(schema="<Your Schema>", question="Find all users with more than 5 transactions")
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)
print(tokenizer.decode(outputs[0]))
```

### Training Details
**Training Data**: The model was fine-tuned on the **Neo4j Text2Cypher dataset (2024v1)**.  
**Training Procedure**:
- **Preprocessing**: Tokenized using the **Alpaca format**.
- **Training Hyperparameters**:
  - `batch_size=2`
  - `gradient_accumulation_steps=4`
  - `num_train_epochs=3`
  - `learning_rate=2e-4`
  - `fp16=True`

### Evaluation
#### Testing Data
- Used the **Neo4j Text2Cypher 2024v1 test split**.

#### Factors
- Model performance was measured on **accuracy of Cypher query generation**.

#### Metrics
- **Exact Match** with ground truth Cypher queries.
- **Execution Success Rate** on a test Neo4j instance.

#### Results
- **High accuracy** for standard database queries.
- **Some errors in complex queries requiring multi-hop reasoning**.

### Environmental Impact
**Hardware Type**: Tesla T4 (Google Colab)  
**Hours Used**: ~7.71 minutes  
**Cloud Provider**: Google Colab  
**Compute Region**: N/A  
**Carbon Emitted**: Estimated using ML Impact calculator  

### Technical Specifications
#### Model Architecture and Objective
- Based on **Llama-3.1 8B** with **LoRA fine-tuning**.

#### Compute Infrastructure
- Fine-tuned using **Unsloth** for efficient training and inference.

#### Hardware
- **GPU**: Tesla T4  
- **Max Reserved Memory**: ~7.922 GB  

#### Software
- **Libraries Used**: `unsloth`, `transformers`, `TRL`, `datasets`

### Citation [optional]
**BibTeX:**
```
@article{llama3.1-8b-cypher,
  author    = {Azzedde},
  title     = {Llama3.1-8B-Cypher: A Cypher Query Generation Model},
  year      = {2025},
  url       = {https://huggingface.co/Azzedde/llama3.1-8b-text2cypher}
}
```

**APA:**  
Azzedde. (2025). *Llama3.1-8B-Cypher: A Cypher Query Generation Model*. Retrieved from [Hugging Face](https://huggingface.co/Azzedde/llama3.1-8b-text2cypher)

### More Information
For questions, reach out via **Hugging Face discussions** or GitHub issues.

### Model Card Authors
- **Azzedde** (GitHub: Azzedde)

### Model Card Contact
**Contact**: [Hugging Face Profile](https://huggingface.co/Azzedde)