---
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))
```