Azzedde
/

llama3.1-8b-text2cypher

@@ -4,199 +4,156 @@ tags:
 - unsloth
 - trl
 - sft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
 ### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
 #### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
 #### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
 #### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
 #### Hardware
-[More Information Needed]
 #### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 **BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 - 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)