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Model: Ashish-Ranjan/slm-125m-legal-instruct
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model: thesreedath/slm-125m-base
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- legal
+- finance
+- instruction-tuned
+- slm
+---
+
+# slm-125m-legal-instruct
+
+A 125M-parameter Llama-architecture small language model, instruction-tuned for
+grounded legal & financial question answering.
+
+- **Base:** [thesreedath/slm-125m-base](https://huggingface.co/thesreedath/slm-125m-base) (125M, pretrained on legal/financial + web text, 10 epochs)
+- **Fine-tuning:** SFT on ~7,760 RAFT-style grounded Q&A pairs across 4 task types
+  (grounded QA, summarization, extraction, rewriting), synthesized with Gemini and
+  quality-filtered (LLM judge + embedding dedup).
+- **Chat format:** `<|system|>` / `<|user|>` / `<|assistant|>`; terminate with `<|eos|>` (id 1).
+- **Context length:** 1024 tokens. Answer-only loss (context + question -> answer).
+
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("Ashish-Ranjan/slm-125m-legal-instruct")
+model = AutoModelForCausalLM.from_pretrained("Ashish-Ranjan/slm-125m-legal-instruct")
+sys = "You are a helpful legal and financial assistant. Answer using only the provided context."
+prompt = "<|system|>" + sys + "<|user|>" + context + "\n\n" + question + "<|assistant|>"
+ids = tok(prompt, return_tensors="pt").input_ids
+out = model.generate(ids, max_new_tokens=200, do_sample=True, temperature=0.7, eos_token_id=1)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+```