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README.md

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+---
+language: en
+tags:
+  - cypher
+  - neo4j
+  - graph-rag
+  - text2cypher
+  - phi-3
+  - fine-tuned
+  - nlp
+license: mit
+base_model: microsoft/Phi-3-mini-4k-instruct
+---
+
+# NL → Cypher · Graph RAG (Phi-3-mini)
+
+Fine-tuned **[Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)**
+to convert natural language questions into Neo4j Cypher queries for Graph RAG pipelines.
+
+## Example
+
+| Input | Output |
+|-------|--------|
+| Who acted in Inception? | `MATCH (p:Person)-[:ACTED_IN]->(m:Movie {title: 'Inception'}) RETURN p.name` |
+| Top 3 highest rated movies? | `MATCH (m:Movie) RETURN m.title, m.rating ORDER BY m.rating DESC LIMIT 3` |
+| People older than 30 in Chennai? | `MATCH (p:Person) WHERE p.age > 30 AND p.city = 'Chennai' RETURN p.name, p.age` |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model     = AutoModelForCausalLM.from_pretrained("AtalGun/nl2cypher-phi3")
+tokenizer = AutoTokenizer.from_pretrained("AtalGun/nl2cypher-phi3", use_fast=False)
+
+SCHEMA = """
+Node types:
+  - Person   { name, age, email, city }
+  - Movie    { title, year, genre, rating }
+  - Company  { name, industry, country }
+Relationships:
+  - (Person)-[:ACTED_IN]->(Movie)
+  - (Person)-[:DIRECTED]->(Movie)
+  - (Person)-[:WORKS_AT]->(Company)
+  - (Person)-[:KNOWS]->(Person)
+"""
+
+def ask(question: str) -> str:
+    prompt = (
+        f"<|system|>\nYou are a Cypher query generator.\n"
+        f"Schema:\n{SCHEMA}<|end|>\n"
+        f"<|user|>\n{question}<|end|>\n"
+        f"<|assistant|>\n"
+    )
+    inputs = tokenizer(prompt, return_tensors="pt")
+    inputs.pop("token_type_ids", None)
+    out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
+    return tokenizer.decode(
+        out[0][inputs["input_ids"].shape[1]:],
+        skip_special_tokens=True
+    ).strip()
+
+print(ask("Who acted in Inception?"))
+# MATCH (p:Person)-[:ACTED_IN]->(m:Movie {title: 'Inception'}) RETURN p.name
+```
+
+## Training Details
+
+| | |
+|---|---|
+| Base model | microsoft/Phi-3-mini-4k-instruct |
+| Method | QLoRA (r=16, alpha=32) |
+| Framework | Unsloth + TRL SFTTrainer |
+| Dataset | neo4j/text2cypher-2024v1 + custom seed examples |
+| Hardware | Google Colab T4 GPU |
+| Epochs | 3 |
+| Precision | fp16 |
+
+## Graph Schema
+
+The model was fine-tuned on a Person / Movie / Company knowledge graph.
+Inject your own schema into the system prompt to adapt it to any Neo4j graph.
+
+## Limitations
+
+- Best results when graph schema is explicitly provided in the system prompt
+- Designed for Neo4j Cypher — not tested on other graph query languages