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sahuabinash
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Model Details
Model Provider
sahuabinash
Model Tree
thesreedath/slm-125m-base
Input Modalities
Output Modalities
Supported Functionality
7,521 chat-format (system/user/assistant) examples generated from a legal/SEC/web
corpus using Gemini (gemini-3.1-flash-lite) as teacher, filtered by an LLM-as-judge
pass for grounding/faithfulness, deduplicated by embedding similarity, covering four
task types: grounded QA, summarization, extraction, and rewriting.
Full fine-tune (not LoRA), 3 epochs, 1x H100, dynamic per-batch padding, loss masked to the assistant response only. 354 steps, ~1.6M real tokens processed, 69 seconds total. Validation perplexity 9.13 on the Q&A task.
Prompts use the same special-token format as the base model:
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("sahuabinash/slm-125m-finetuned-qa")model = AutoModelForCausalLM.from_pretrained("sahuabinash/slm-125m-finetuned-qa")prompt = "<|bos|><|system|>Answer the question using only the given passage.<|user|>What is a plaintiff?<|assistant|>"inputs = tok(prompt, return_tensors="pt")out = model.generate(**inputs, max_new_tokens=80)print(tok.decode(out[0], skip_special_tokens=True))