---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model: thesreedath/slm-125m-base
tags:
- llama
- legal
- financial
- small-language-model
- sft
- instruction-tuning
---

# legal-slm-125m-sft

A 125M-parameter Llama-architecture model **supervised-fine-tuned to answer
legal & financial questions**. It is the instruction-tuned sibling of a
from-scratch base model: where the base model only *continues* text, this model
*responds* to a question.

- **Base model:** [`thesreedath/slm-125m-base`](https://huggingface.co/thesreedath/slm-125m-base) (125M, pretrained 10 epochs)
- **Fine-tuned on:** 5,846 grounded legal/financial Q&A pairs (synthetic, quality-filtered)
- **Validation loss:** 2.06 (cross-entropy on held-out Q&A)

> **Base-capacity model.** At 125M parameters this is a study in end-to-end LLM
> engineering, not a production assistant. It learns the *shape* of a good answer
> and often gets the gist right, but it will **fabricate** case names, statutes,
> and figures. **Never** use its output as legal, financial, or factual advice.

## What it does

The base model, given "In a breach of contract claim, the plaintiff…", would ramble
onward. This model, asked a question, answers it:

> **Q:** In a breach of contract claim, what must the plaintiff prove?
> **A:** The plaintiff must show: (1) a contract; (2) an agreement to perform a
> specific act; (3) an obligation to perform…

## Chat format

The tokenizer has role special tokens but **no chat template**, so format prompts
manually as `<|bos|><|system|>{system}<|user|>{question}<|assistant|>` and let the
model complete the answer up to `<|eos|>`:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("jonam-ai/legal-slm-125m-sft")
model = AutoModelForCausalLM.from_pretrained("jonam-ai/legal-slm-125m-sft", torch_dtype=torch.bfloat16)

system = "You are a knowledgeable legal and financial assistant. Answer accurately and concisely."
question = "What is the purpose of a Form 10-K filing?"

def sid(t): return tok.convert_tokens_to_ids(t)
ids = (tok("<|bos|>", add_special_tokens=False)["input_ids"]
       + [sid("<|system|>")] + tok(system, add_special_tokens=False)["input_ids"]
       + [sid("<|user|>")]   + tok(question, add_special_tokens=False)["input_ids"]
       + [sid("<|assistant|>")])
out = model.generate(torch.tensor([ids]), max_new_tokens=120, do_sample=True,
                     temperature=0.7, top_p=0.9, eos_token_id=sid("<|eos|>"),
                     pad_token_id=sid("<|pad|>"))
print(tok.decode(out[0][len(ids):], skip_special_tokens=True))
```

## Training data

5,846 grounded question–answer pairs, synthesized with a **teacher-LLM
distillation** pipeline over a cleaned corpus of US case law, SEC filings, and
educational web text:

1. **Chunk** the corpus into ~800-token passages.
2. **Generate** (Gemini Flash-Lite): write self-contained Q&A answerable *only*
   from the passage, balanced across task types (QA, extraction, summarization,
   rewrite) and difficulty (easy → hard).
3. **Judge** (Gemini Flash as LLM-as-judge): keep only pairs that are grounded,
   correct, and self-contained (score ≥ 4/5). ~78% pass.
4. **Dedup** (exact + MinHash-LSH near-duplicate removal).
5. **Format** as chat and **tokenize** with the base model's own tokenizer, with
   **loss computed only on the answer tokens**.

Mix: case-law 45% · SEC 45% · web 10%. Split: 5,554 train / 292 val.

## Training

| | |
|---|---|
| Method | full fine-tune (not LoRA) |
| Hardware | 1 × NVIDIA L4 |
| Epochs | 2 |
| Tokens seen | ~1.0M |
| LR | 3e-5, cosine decay, 3% warmup |
| Precision | bf16 compute, fp32 master weights |
| Time | ~80 seconds |

## Limitations

125M parameters, English only, 1,024-token context, not aligned/RLHF'd. It imitates
the form of grounded answers learned from a synthetic dataset; factual reliability
is limited by model size. Not legal or financial advice.