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Model: Azzedde/llama3.1-8b-text2cypher Source: Original Platform
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---
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library_name: transformers
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tags:
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- unsloth
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- trl
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- sft
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license: mit
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datasets:
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- neo4j/text2cypher-2024v1
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language:
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- en
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base_model:
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- unsloth/Llama-3.1-8B-Instruct
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pipeline_tag: text-generation
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---
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## Model Card for Llama3.1-8B-Cypher
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### Model Details
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**Model Description**
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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.
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**Developed by**: Azzedine (GitHub: Azzedde)
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**Funded by [optional]**: N/A
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**Shared by [optional]**: Azzedde
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**Model Type**: Large Language Model (LLM) optimized for Cypher query generation
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**Language(s) (NLP)**: English
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**License**: Apache 2.0
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**Finetuned from model [optional]**: Meta-Llama-3.1-8B-Instruct
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### Model Sources
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**Repository**: [Hugging Face](https://huggingface.co/Azzedde/llama3.1-8b-text2cypher)
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**Paper [optional]**: N/A
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**Demo [optional]**: N/A
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### Uses
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#### Direct Use
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This model is designed for generating **Cypher queries** for **Neo4j databases** based on natural language inputs. It can be used in:
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- Database administration
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- Knowledge graph construction
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- Query automation for structured data retrieval
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#### Downstream Use [optional]
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- Integrating into **LLM-based database assistants**
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- Automating **graph database interactions** in enterprise applications
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- Enhancing **semantic search and recommendation systems**
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#### Out-of-Scope Use
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- General NLP tasks unrelated to graph databases
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- Applications requiring strong factual accuracy outside Cypher query generation
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### Bias, Risks, and Limitations
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- The model may **generate incorrect or suboptimal Cypher queries**, especially for **complex database schemas**.
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- The model has not been trained to **validate or optimize queries**, so users should manually **verify generated queries**.
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- Limited to **English-language inputs** and **Neo4j graph database use cases**.
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### Recommendations
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Users should be aware of:
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- The importance of **validating model-generated queries** before execution.
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- The **potential for biases** in database schema interpretation.
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- The need for **fine-tuning on domain-specific datasets** for best performance.
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### How to Get Started with the Model
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Use the following code to load and use the model:
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```python
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from unsloth import FastLanguageModel
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("Azzedde/llama3.1-8b-text2cypher")
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model = FastLanguageModel.from_pretrained("Azzedde/llama3.1-8b-text2cypher")
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# Example inference
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cypher_prompt = """Below is a database Neo4j schema and a question related to that database. Write a Cypher query to answer the question.
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### Schema:
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{schema}
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### Question:
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{question}
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### Cypher:
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"""
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input_text = cypher_prompt.format(schema="<Your Schema>", question="Find all users with more than 5 transactions")
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inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)
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print(tokenizer.decode(outputs[0]))
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```
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### Training Details
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**Training Data**: The model was fine-tuned on the **Neo4j Text2Cypher dataset (2024v1)**.
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**Training Procedure**:
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- **Preprocessing**: Tokenized using the **Alpaca format**.
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- **Training Hyperparameters**:
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- `batch_size=2`
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- `gradient_accumulation_steps=4`
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- `num_train_epochs=3`
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- `learning_rate=2e-4`
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- `fp16=True`
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### Evaluation
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#### Testing Data
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- Used the **Neo4j Text2Cypher 2024v1 test split**.
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#### Factors
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- Model performance was measured on **accuracy of Cypher query generation**.
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#### Metrics
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- **Exact Match** with ground truth Cypher queries.
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- **Execution Success Rate** on a test Neo4j instance.
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#### Results
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- **High accuracy** for standard database queries.
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- **Some errors in complex queries requiring multi-hop reasoning**.
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### Environmental Impact
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**Hardware Type**: Tesla T4 (Google Colab)
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**Hours Used**: ~7.71 minutes
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**Cloud Provider**: Google Colab
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**Compute Region**: N/A
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**Carbon Emitted**: Estimated using ML Impact calculator
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### Technical Specifications
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#### Model Architecture and Objective
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- Based on **Llama-3.1 8B** with **LoRA fine-tuning**.
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#### Compute Infrastructure
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- Fine-tuned using **Unsloth** for efficient training and inference.
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#### Hardware
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- **GPU**: Tesla T4
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- **Max Reserved Memory**: ~7.922 GB
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#### Software
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- **Libraries Used**: `unsloth`, `transformers`, `TRL`, `datasets`
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### Citation [optional]
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**BibTeX:**
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```
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@article{llama3.1-8b-cypher,
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author = {Azzedde},
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title = {Llama3.1-8B-Cypher: A Cypher Query Generation Model},
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year = {2025},
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url = {https://huggingface.co/Azzedde/llama3.1-8b-text2cypher}
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}
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```
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**APA:**
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Azzedde. (2025). *Llama3.1-8B-Cypher: A Cypher Query Generation Model*. Retrieved from [Hugging Face](https://huggingface.co/Azzedde/llama3.1-8b-text2cypher)
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### More Information
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For questions, reach out via **Hugging Face discussions** or GitHub issues.
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### Model Card Authors
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- **Azzedde** (GitHub: Azzedde)
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### Model Card Contact
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**Contact**: [Hugging Face Profile](https://huggingface.co/Azzedde)
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config.json
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"architectures": [
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}
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||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|finetune_right_pad_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
|
||||
size 17209920
|
||||
2067
tokenizer_config.json
Normal file
2067
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user