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oscardean
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Model Details
Model Provider
oscardean
Model Tree
HuggingFaceTB/SmolLM2-135M-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "oscardean/smollm2-135m-text2cypher" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id) messages = [ { "role": "system", "content": ( "You translate natural-language questions into Cypher queries. " "Use only the supplied graph schema and return only the Cypher query." ), }, { "role": "user", "content": ( "Graph schema:\n" "Person {name: STRING}\n" "Movie {title: STRING, year: INTEGER}\n" "(Person)-[:DIRECTED]->(Movie)\n\n" "Question:\n" "Which movies did Christopher Nolan direct before 2010?" ), },] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt",) outputs = model.generate( inputs, max_new_tokens=192, do_sample=False,) prediction = tokenizer.decode( outputs[0, inputs.shape[1]:], skip_special_tokens=True,) print(prediction)| Hyperparameter | Value |
|---|---|
| Training samples | 1,000 |
| Validation samples | 75 |
| Epochs | 3 |
| Learning rate | 5e-5 |
| Batch size | 2 |
| Gradient accumulation | 4 |
| Effective batch size | 8 |
| Weight decay | 0.01 |
| Warmup ratio | 0.05 |
| Maximum sequence length | 800 |
| Decoding | Greedy |
| Checkpoint selection | Lowest validation loss |
Evaluated on the 50-sample test split.
| Metric | Base | Fine-tuned |
|---|---|---|
| Basic query structure | 2.00% | 100.00% |
| Token F1 | 12.35% | 55.20% |
| Node-label agreement | 0.00% | 58.00% |
| Component match rate | 29.20% | 49.60% |
| Normalized exact match | 0.00% | 0.00% |
from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "oscardean/smollm2-135m-text2cypher" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id) messages = [ { "role": "system", "content": ( "You translate natural-language questions into Cypher queries. " "Use only the supplied graph schema and return only the Cypher query." ), }, { "role": "user", "content": ( "Graph schema:\n" "Person {name: STRING}\n" "Movie {title: STRING, year: INTEGER}\n" "(Person)-[:DIRECTED]->(Movie)\n\n" "Question:\n" "Which movies did Christopher Nolan direct before 2010?" ), },] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt",) outputs = model.generate( inputs, max_new_tokens=192, do_sample=False,) prediction = tokenizer.decode( outputs[0, inputs.shape[1]:], skip_special_tokens=True,) print(prediction)