Instructions to use Anugya/text2cypher-smollm2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anugya/text2cypher-smollm2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Anugya/text2cypher-smollm2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Anugya/text2cypher-smollm2")
model = AutoModelForMultimodalLM.from_pretrained("Anugya/text2cypher-smollm2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anugya/text2cypher-smollm2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anugya/text2cypher-smollm2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anugya/text2cypher-smollm2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Anugya/text2cypher-smollm2

SGLang

How to use Anugya/text2cypher-smollm2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Anugya/text2cypher-smollm2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anugya/text2cypher-smollm2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Anugya/text2cypher-smollm2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anugya/text2cypher-smollm2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Anugya/text2cypher-smollm2 with Docker Model Runner:
```
docker model run hf.co/Anugya/text2cypher-smollm2
```

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

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

Downloads last month: 150

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Anugya/text2cypher-smollm2

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Finetuned

(342)

this model

Anugya
/

text2cypher-smollm2