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nalandabh
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Model Details
Model Provider
nalandabh
Model Tree
HuggingFaceTB/SmolLM2-360M-Instruct
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "nalandabh/bihar-360m"tok = AutoTokenizer.from_pretrained(repo)model = AutoModelForCausalLM.from_pretrained(repo) SYSTEM = ("You are a helpful assistant for questions about Bihar. Answer only from the provided context. " "If the context does not contain the answer, say you don't have that information.")passage = "Paste a passage about Bihar here."question = "Your question here?" messages = [ {"role": "system", "content": SYSTEM}, {"role": "user", "content": f"Context:\n{passage}\n\nQuestion: {question}"},]ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")out = model.generate(ids, max_new_tokens=150, do_sample=False)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))Use this exact prompt format. When the passage lacks the answer, the reply is: "I don't have that information in the provided text."
Fine-tuned from SmolLM2-360M-Instruct with LoRA. Question-answer pairs were generated from Bihar text chunks with Qwen3-8B and filtered for accuracy. English only.
Text sources: English Wikipedia (CC BY-SA 4.0), Wikidata (CC0) and pages from Grokipedia (grokipedia.com). See ATTRIBUTION.md for the page list.
v0.1, an early version. English only. Not for legal, medical or official decisions.