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shivamfet
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Model Details
Model Provider
shivamfet
Model Tree
shivamfet/slm-125m-base
Input Modalities
Output Modalities
Supported Functionality
Reads a source passage and answers extractive questions about it — who, what, where, which claims, what a clause requires, what a court held. It pulls the answer from the passage and states it directly, rather than answering from memory.
At ~125M parameters this is a small model with real limits, stated honestly:
| Method | Supervised fine-tuning (answer-only loss mask) |
| Data | 13,774 grounded QA pairs, teacher-generated + LLM-judged for groundedness, semantically deduplicated |
| Sources | U.S. case law, SEC filings, FineWeb-Edu passages |
| Schedule | 2 epochs, 1×H100, ~4 min, AdamW, cosine LR 2e-5→2e-6 |
| Selection | Epoch 2 chosen by held-out val loss (later epochs overfit) |
| Val perplexity | 2.60 (answer tokens) |
| Architecture | Llama, 12L / 768d / 12h, 16,384 vocab, 1024 context |
python
from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("shivamfet/slm-125m-instruct")model = AutoModelForCausalLM.from_pretrained("shivamfet/slm-125m-instruct") passage = ("The court affirmed the conviction but remanded for resentencing " "because the trial judge improperly considered the defendant's silence.")question = "Why did the court remand the case?" messages = [ {"role": "system", "content": "Answer the question using only the passage."}, {"role": "user", "content": passage + "\n\n" + question},]prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)enc = tok(prompt, return_tensors="pt", return_token_type_ids=False) # Greedy decoding, NO repetition penalty: extractive QA must copy spans from the# passage, and a penalty >1 pushes the model off the grounded answer.eos = tok.convert_tokens_to_ids("<|eos|>")out = model.generate(**enc, max_new_tokens=128, do_sample=False, eos_token_id=eos, pad_token_id=eos)print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True).strip())# -> "The court remanded because the trial judge improperly considered the silence."from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("shivamfet/slm-125m-instruct")model = AutoModelForCausalLM.from_pretrained("shivamfet/slm-125m-instruct") passage = ("The court affirmed the conviction but remanded for resentencing " "because the trial judge improperly considered the defendant's silence.")question = "Why did the court remand the case?" messages = [ {"role": "system", "content": "Answer the question using only the passage."}, {"role": "user", "content": passage + "\n\n" + question},]prompt = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)enc = tok(prompt, return_tensors="pt", return_token_type_ids=False) # Greedy decoding, NO repetition penalty: extractive QA must copy spans from the# passage, and a penalty >1 pushes the model off the grounded answer.eos = tok.convert_tokens_to_ids("<|eos|>")out = model.generate(**enc, max_new_tokens=128, do_sample=False, eos_token_id=eos, pad_token_id=eos)print(tok.decode(out[0][enc["input_ids"].shape[1]:], skip_special_tokens=True).strip())# -> "The court remanded because the trial judge improperly considered the silence."