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Model: Azzedde/llama3.1-8b-text2cypher Source: Original Platform
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+---
+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)