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kv-rane
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Model Details
Model Provider
kv-rane
Model Tree
HuggingFaceTB/SmolLM2-135M-Instruct
Input Modalities
Output Modalities
Supported Functionality
Evaluated on 50 test examples:
Token F1 improved 64% when scaling from 200 to 1000 training examples (16.7% → 27.4%), confirming genuine learning.
Note - 200 sample training was done just to check cpu performance and loss behaviour
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained( "HuggingFaceTB/SmolLM2-135M-Instruct")model = PeftModel.from_pretrained(base_model, "kv-rane/text2cypher-smollm2")tokenizer = AutoTokenizer.from_pretrained("kv-rane/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))This model uses LoRA instead of full fine-tuning:
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base_model = AutoModelForCausalLM.from_pretrained( "HuggingFaceTB/SmolLM2-135M-Instruct")model = PeftModel.from_pretrained(base_model, "kv-rane/text2cypher-smollm2")tokenizer = AutoTokenizer.from_pretrained("kv-rane/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))