GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
shivamfet
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
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, AutoTokenizertok = 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."