Azzedde/llama3.1-8b-text2cypher

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
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:

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

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