---
library_name: transformers
base_model: HuggingFaceTB/SmolLM2-135M-Instruct
tags:
- text2cypher
- cypher
- graph
- fine-tuned
language:
- en
license: apache-2.0
datasets:
- RomanTeucher/text2cypher-curated
---

# Text2Cypher — SmolLM2-135M Fine-tuned

A fine-tuned version of `SmolLM2-135M-Instruct` that generates Cypher queries from natural language questions and a graph schema.

## Model Details

- **Base model:** HuggingFaceTB/SmolLM2-135M-Instruct
- **Model type:** Causal Language Model
- **Language:** English
- **License:** Apache 2.0
- **Finetuned by:** Anugya Sahu

## Training Data

- **Dataset:** `RomanTeucher/text2cypher-curated`
- 1000 training samples, 75 validation, 50 test
- Each sample contains a graph schema, a natural language question, and a target Cypher query

## How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Anugya/text2cypher-smollm2")
tokenizer = AutoTokenizer.from_pretrained("Anugya/text2cypher-smollm2")
tokenizer.pad_token = tokenizer.eos_token

schema = "Movie {title, year}, Person {name}, (Person)-[:DIRECTED]->(Movie)"
question = "Which movies did Christopher Nolan direct before 2010?"

prompt = f"""### Schema:
{schema}

### Question:
{question}

### Cypher:"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)
generated = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))
```

## Training Details

- **Full fine-tune** — all weights updated, no LoRA
- **Epochs:** 3
- **Learning rate:** 2e-4
- **Batch size:** 4
- **Max token length:** 256
- **Hardware:** CPU (Apple M-series)
- **Precision:** float32

## Evaluation

Evaluated on 50 test samples using:

- **Exact Match** — strict comparison after lowercasing and stripping
- **Token F1** — token overlap between prediction and ground truth

## Limitations

- 135M parameter model — generates Cypher that looks right but often isn't
- No query execution validation against a real Neo4j database
- May struggle with complex schemas or multi-hop queries
- Trained on CPU with limited epochs — larger training would improve results