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Sarath569
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Model Details
Model Provider
Sarath569
Model Tree
Sarath569/slm-125m-legal-base
Input Modalities
Output Modalities
Supported Functionality
text
<|system|>You are a legal and financial assistant. Answer the question using ONLY the provided context. If the answer is not contained in the context, say you do not have enough information to answer.<|user|><context>{passage the answer must come from}</context>Question: {your question}<|assistant|>{model answer}<|eos|>Teacher-distilled (OpenAI gpt-4o-mini) grounded Q&A over sampled corpus passages, each answer validated by an LLM judge for correctness + faithfulness, semantically deduplicated, with ~10% refusal examples. Loss on the assistant answer tokens only; LR 2e-5 cosine. Final val loss 0.9955 (ppl 2.71).
python
from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("Sarath569/slm-125m-legal-raft")model = AutoModelForCausalLM.from_pretrained("Sarath569/slm-125m-legal-raft") system = ("You are a legal and financial assistant. Answer the question using ONLY the " "provided context. If the answer is not contained in the context, say you do " "not have enough information to answer.")context = "The Company reported net revenue of $4.2 billion for fiscal 2023, up 12%..."question = "What was net revenue for fiscal 2023?"prompt = (f"<|system|>\n{system}\n<|user|>\n<context>\n{context}\n</context>\n\n" f"Question: {question}\n<|assistant|>\n")ids = tok(prompt, add_special_tokens=False, return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=120, do_sample=False)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))This is a ~125M model. It reads a provided passage and answers from it fairly well without fabricating facts, which is its purpose — but at this scale it can be imprecise on exact figures and its refusal behavior is imperfect (it may dodge rather than cleanly decline when the answer is absent). It must not be used for legal, financial, or any real-world advice. No alignment/RLHF. Intended for research and as a small grounded-QA fine-tuning demonstration, not production use.
from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("Sarath569/slm-125m-legal-raft")model = AutoModelForCausalLM.from_pretrained("Sarath569/slm-125m-legal-raft") system = ("You are a legal and financial assistant. Answer the question using ONLY the " "provided context. If the answer is not contained in the context, say you do " "not have enough information to answer.")context = "The Company reported net revenue of $4.2 billion for fiscal 2023, up 12%..."question = "What was net revenue for fiscal 2023?"prompt = (f"<|system|>\n{system}\n<|user|>\n<context>\n{context}\n</context>\n\n" f"Question: {question}\n<|assistant|>\n")ids = tok(prompt, add_special_tokens=False, return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=120, do_sample=False)print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))