A fine-tune of HuggingFaceTB/SmolLM2-135M-Instruct that turns a natural-language question + a graph schema into a Cypher query. Trained with LoRA on RomanTeucher/text2cypher-curated (1000 train / 75 val / 50 test); the adapters are merged into the base weights, so this is a standalone full model that loads like any HF checkpoint — no PEFT needed. This model is a submission for an interview.

Usage

The model expects the same ChatML prompt it was trained on: a fixed system message plus a user turn of Schema: + Question:.

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "rizwan261/smollm2-135m-text2cypher"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

SYSTEM = ("You are a text-to-Cypher engine for Neo4j. Given a graph schema and a "
          "question, output a single valid Cypher query that answers the question. "
          "Use only labels, relationship types and properties that appear in the "
          "schema. Output the Cypher query and nothing else: no explanation, no "
          "comments, no markdown code fences.")

schema = "Graph schema: Relevant node labels and their properties (with datatypes) are:\nUpdateDate {update_date: DATE}"
question = "Which nodes are connected to UpdateDate where update_date is 2008-01-29, and also to another node?"
messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content": f"Schema:\n{schema}\n\nQuestion:\n{question}"},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tok.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Training

LoRA (r=16, α=32, dropout=0.05) on the attention + MLP projections, completion-only loss (the prompt is masked, loss is on the target Cypher only), then merged.


epochs	20
learning rate	5e-5, cosine, 5% warmup
effective batch	16 (bs 4 × grad-accum 4)
max length	1024
best val loss	~0.32 (by `eval_loss`)

Evaluation

Test split (n=50), greedy decoding. Translation / structural metrics over the whole split — surface proxies for correctness:

metric	base	this model
exact_match	0.00	0.26
structural_f1	0.18	0.81
google_bleu	0.07	0.70
well_formed	0.00	0.98

Execution-based evaluation against the live Neo4j demo databases, on the 18/50 samples that carry a database reference — predicted and gold queries are run and their result sets compared. This is the honest measure, and each stricter layer peels back the previous one's optimism:

how strict	metric	this model
real parser + live schema	CyVer KG-Valid-Query	0.61
runs on the DB	query executes	0.72
returns the right rows	exec ExactMatch (non-trivial)	0.00

The surface metrics jump, but execution accuracy stays at zero: the model writes well-formed, schema-valid, runnable Cypher that returns the wrong data.

Limitations

A 135M model produces Cypher that often looks right but isn't: only ~26% are exact matches, and non-trivial execution accuracy is ~0 — well-formed, runnable queries that return the wrong rows. It reliably gets node labels, clauses and the query skeleton, but struggles with same-node/co-reference patterns, multi-stage aggregation, and occasionally hallucinates functions. Execution numbers come from a small (n=18) subset run against live demo databases, so they can drift.

License

Apache-2.0, inherited from the SmolLM2 base model.