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Model: sahuabinash/slm-125m-finetuned-qa
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama
+- fine-tuned
+- legal
+- finance
+- qa
+base_model: thesreedath/slm-125m-base
+---
+
+# slm-125m-finetuned-qa
+
+A 125.8M-parameter LLaMA-architecture model, fine-tuned from
+[thesreedath/slm-125m-base](https://huggingface.co/thesreedath/slm-125m-base) on a
+grounded Q&A instruction dataset covering legal, financial, and general text.
+
+## Training data
+
+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.
+
+## Training
+
+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.
+
+## Usage
+
+Prompts use the same special-token format as the base model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = 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))
+```