base_model: google/gemma-2-9b-it library_name: peft license: apache-2.0 datasets:

• neo4j/text2cypher-2024v1 language:
• en pipeline_tag: text2text-generation tags:
• neo4j
• cypher
• text2cypher

Model Card for Model ID

Model Details

Model Description

This model serves as a demonstration of how fine-tuning foundational models using the Neo4j-Text2Cypher(2024) Dataset (link) 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 and Link2

Have ideas or insights? Contact us: Neo4j/Team-GenAI

Bias, Risks, and Limitations

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

Training Details

Training Procedure

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

Training Hyperparameters

• 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, )

Framework versions

• PEFT 0.12.0

Example Cypher generation

from peft import PeftModel, PeftConfig
import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)


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: "
)



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

def _postprocess_output_cypher(output_cypher: str) -> str:
    # Remove any explanation. E.g.  MATCH...\n\n**Explanation:**\n\n -> MATCH...
    # Remove cypher indicator. E.g.```cypher\nMATCH...```` --> MATCH...
    # Note: Possible to have both:
    #   E.g. ```cypher\nMATCH...````\n\n**Explanation:**\n\n --> MATCH...
    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

# Model
model_name = "neo4j/text2cypher-gemma-2-9b-it-finetuned-2024v1"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    torch_dtype=torch.bfloat16,
    attn_implementation="eager",
    low_cpu_mem_usage=True,
)

# Question
question = "What are the movies of Tom Hanks?"
schema = "(:Actor)-[:ActedIn]->(:Movie)" # Check the NOTE below on creating your own schemas
new_message = prepare_chat_prompt(question=question, schema=schema)
prompt = tokenizer.apply_chat_template(new_message, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt", padding=True)

# Any other parameters
model_generate_parameters = {
    "top_p": 0.9,
    "temperature": 0.2,
    "max_new_tokens": 512,
    "do_sample": True,
    "pad_token_id": tokenizer.eos_token_id,
}

inputs.to(model.device)
model.eval()
with torch.no_grad():
    tokens = model.generate(**inputs, **model_generate_parameters)
    tokens = tokens[:, inputs.input_ids.shape[1] :]
    raw_outputs = tokenizer.batch_decode(tokens, skip_special_tokens=True)
    outputs = [_postprocess_output_cypher(output) for output in raw_outputs]
    
print(outputs)
> ["MATCH (a:Actor {Name: 'Tom Hanks'})-[:ActedIn]->(m:Movie) RETURN m"]

NOTE on creating your own schemas:

• In the dataset we used, the schemas are already provided. They are created either by
  • Directly using the schema the input data source provided OR
  • Creating schema using neo4j-graphrag package (Check: SchemaReader.get_schema(...) function)
• In your own Neo4j database, you can utilize neo4j-graphrag package::SchemaReader functions