---
license: gemma
library_name: transformers
pipeline_tag: text2text-generation
tags:
  - conversational
  - neo4j
  - cypher
  - text2cypher
base_model: google/gemma-2-9b-it
datasets:
  - neo4j/text2cypher-2024v1
language:
  - en
---

# Model Card for Model ID

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## Model Details

### Model Description

This model serves as a demonstration of how fine-tuning foundational models using the Neo4j-Text2Cypher(2024) Dataset ([link](https://huggingface.co/datasets/neo4j/text2cypher-2024v1)) can enhance performance on the Text2Cypher task.\
Please **note**, this is part of ongoing research and exploration, aimed at highlighting the dataset's potential rather than a production-ready solution.


**Base model:** google/gemma-2-9b-it \
**Dataset:** neo4j/text2cypher-2024v1

An overview of the finetuned models and benchmarking results are shared at [Link1](https://medium.com/p/d77be96ab65a) and [Link2](https://medium.com/p/b2203d1173b0)

Have ideas or insights? Contact us: [Neo4j/Team-GenAI](mailto:team-gen-ai@neo4j.com)


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## Bias, Risks, and Limitations

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We need to be cautious about a few risks:
* In our evaluation setup, the training and test sets come from the same data distribution (sampled from a larger dataset). If the data distribution changes, the results may not follow the same pattern.
* The datasets used were gathered from publicly available sources. Over time, foundational models may access both the training and test sets, potentially achieving similar or even better results.

Also check the related blogpost:[Link](Thttps://medium.com/p/b2203d1173b0)

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 ## Training Details

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### Training Procedure

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Used RunPod with following setup:

* 1 x A100 PCIe
* 31 vCPU 117 GB RAM
* runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04
* On-Demand - Secure Cloud
* 60 GB Disk
* 60 GB Pod Volume
<!-- * ~16 hours
* $30 -->

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#### Training Hyperparameters

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<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
* lora_config = LoraConfig(
    r=64,
    lora_alpha=64,
    target_modules=target_modules,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)
* sft_config = SFTConfig(
    dataset_text_field=dataset_text_field,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=8,
    dataset_num_proc=16,
    max_seq_length=1600,
    logging_dir="./logs",
    num_train_epochs=1,
    learning_rate=2e-5,
    save_steps=5,
    save_total_limit=1,
    logging_steps=5,
    output_dir="outputs",
    optim="paged_adamw_8bit",
    save_strategy="steps",
)
* bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)
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### Results

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#### Summary -->



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## Environmental Impact -->

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## Technical Specifications [optional]

### Model Architecture and Objective

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### Framework versions

- PEFT 0.12.0

### Example Cypher generation
```
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "DavidLanz/text2cypher-gemma-2-9b-it-finetuned-2024v1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float32,
    device_map="auto",
    low_cpu_mem_usage=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

question = "What are the movies of Tom Hanks?"
schema = "(:Actor)-[:ActedIn]->(:Movie)"

instruction = (
    "Generate Cypher statement to query a graph database. "
    "Use only the provided relationship types and properties in the schema. \n"
    "Schema: {schema} \n Question: {question}  \n Cypher output: "
)
prompt = instruction.format(schema=schema, question=question)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
model.eval()
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512)
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print("Generated Cypher Query:", generated_text)

def prepare_chat_prompt(question, schema):
    chat = [
        {
            "role": "user",
            "content": instruction.format(
                schema=schema, question=question
            ),
        }
    ]
    return chat

def _postprocess_output_cypher(output_cypher: str) -> str:
    # Remove any explanation or formatting markers
    partition_by = "**Explanation:**"
    output_cypher, _, _ = output_cypher.partition(partition_by)
    output_cypher = output_cypher.strip("`\n")
    output_cypher = output_cypher.lstrip("cypher\n")
    output_cypher = output_cypher.strip("`\n ")
    return output_cypher

new_message = prepare_chat_prompt(question=question, schema=schema)
try:
    prompt = tokenizer.apply_chat_template(new_message, add_generation_prompt=True, tokenize=False)
    inputs = tokenizer(prompt, return_tensors="pt", padding=True).to("cuda")
    
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=512)
        chat_generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        final_cypher = _postprocess_output_cypher(chat_generated_text)
        print("Processed Cypher Query:", final_cypher)
except AttributeError:
    print("Error: `apply_chat_template` not supported by this tokenizer. Check compatibility.")

```