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Navyaforaa
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Model Details
Model Provider
Navyaforaa
Model Tree
Navyaforaa/LitGram-1.5B
Input Modalities
Output Modalities
Supported Functionality
LitGram-1.5B is a specialized, domain-adapted language model tailored for English literature scholars, students, UGC NET English aspirants, literary critics, and grammar/syntax researchers.
While standard small language models often struggle with complex literary criticism—frequently hallucinating generic lists of buzzwords or failing on precise metrical scansion—LitGram has been fine-tuned on an extensive, multi-stage curated dataset designed for text-grounded literary hermeneutics, line-by-line prosody, and rigorous syntactic parsing.
x /), metric feet (iamb, trochee, anapest, dactyl, spondee, pyrrhic), and metric substitutions (e.g. initial trochees, feminine endings, caesuras).python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "Navyaforaa/LitGram-1.5B"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype=torch.float16,device_map="auto")messages = [{"role": "system","content": "You are LitGram, a specialized model for English literature and English grammar. Give accurate, text-grounded, clearly explained answers."},{"role": "user","content": "Scan the metre of Shakespeare's Sonnet 18: 'Shall I compare thee to a summer's day?'"}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs,max_new_tokens=512,temperature=0.3,repetition_penalty=1.15)print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))