from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("baderanaas/mistral-cypher-voronode")
tokenizer = AutoTokenizer.from_pretrained("baderanaas/mistral-cypher-voronode")

prompt = "<s>[INST] Your Cypher query task here [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Base Model: Mistral-7B-v0.1
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Data: 33 construction-specific Cypher examples
Domain: Construction Management &amp; Neo4j Graph Databases

Epochs: 3
Batch Size: 16 (effective)
Learning Rate: 2e-4
LoRA Rank: 16
Max Sequence Length: 2048

Schema

The model is trained on construction management schemas including:

Projects, Invoices, Contractors, Contracts
Budgets, Budget Lines, Line Items
Relationships between entities

License

Apache 2.0