Instructions to use Azzedde/llama3.1-8b-text2cypher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azzedde/llama3.1-8b-text2cypher with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Azzedde/llama3.1-8b-text2cypher")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azzedde/llama3.1-8b-text2cypher")
model = AutoModelForCausalLM.from_pretrained("Azzedde/llama3.1-8b-text2cypher")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Azzedde/llama3.1-8b-text2cypher with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azzedde/llama3.1-8b-text2cypher"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azzedde/llama3.1-8b-text2cypher",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Azzedde/llama3.1-8b-text2cypher

SGLang

How to use Azzedde/llama3.1-8b-text2cypher with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Azzedde/llama3.1-8b-text2cypher" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azzedde/llama3.1-8b-text2cypher",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Azzedde/llama3.1-8b-text2cypher" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azzedde/llama3.1-8b-text2cypher",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Azzedde/llama3.1-8b-text2cypher with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Azzedde/llama3.1-8b-text2cypher to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Azzedde/llama3.1-8b-text2cypher to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Azzedde/llama3.1-8b-text2cypher to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Azzedde/llama3.1-8b-text2cypher",
    max_seq_length=2048,
)

Docker Model Runner
How to use Azzedde/llama3.1-8b-text2cypher with Docker Model Runner:
```
docker model run hf.co/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

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Model tree for Azzedde/llama3.1-8b-text2cypher

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

unsloth/Llama-3.1-8B-Instruct

Finetuned

(332)

this model

Azzedde
/

llama3.1-8b-text2cypher

Model Card for Llama3.1-8B-Cypher

Model Details

Model Sources

Uses

Direct Use

Downstream Use [optional]

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

How to Get Started with the Model

Training Details

Evaluation

Testing Data

Factors

Metrics

Results

Environmental Impact

Technical Specifications

Model Architecture and Objective

Compute Infrastructure

Hardware

Software

Citation [optional]

More Information

Model Card Authors

Model Card Contact

Model tree for Azzedde/llama3.1-8b-text2cypher

Dataset used to train Azzedde/llama3.1-8b-text2cypher