---
library_name: transformers
pipeline_tag: text-generation
tags:
  - text-generation
  - causal-lm
  - llama
  - legal
  - finance
  - modal
---

# tirumalaseti/slm-125m-base

This is a **125M-parameter base completion model** trained from scratch on a legal/financial-heavy corpus. It is **not a chatbot**: it was optimized for next-token prediction and works best when prompted with the opening of a sentence or paragraph to continue.

## Model summary

- **Trainable parameters:** 125,847,552 (~125.848M)
- **Architecture:** 12-layer Llama-style decoder, 768 hidden size, 12 attention heads
- **Context length:** 1,024 tokens
- **Tokenizer:** 16,384-token byte-level BPE
- **Training tokens seen:** 2,500,329,472
- **Optimizer steps:** 4,769
- **Completed epochs over the packed train set:** 1.23
- **Final validation loss:** 2.3035
- **Final validation perplexity:** 10.01
- **Reported spend:** $0.00

## Training corpus

The packed corpus used for pretraining contains 2,059,674,624 total tokens:

- **Train:** 2,039,072,768
- **Validation:** 20,601,856

Realized source mix:

- **US case law:** 722,081,792 tokens (35.1%)
- **SEC filings:** 868,714,496 tokens (42.2%)
- **Educational web text:** 468,878,336 tokens (22.8%)

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tirumalaseti/slm-125m-base"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "The plaintiff respectfully moves this Court to"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=96, temperature=0.8, top_p=0.95)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Notes

- This is a **base model**, not an instruction-tuned assistant.
- It is strongest at continuing legal/financial prose in-register.
- The spend figure comes from the latest visible Modal billing report; Modal billing report may lag; this value reflects the latest locally visible report..