2.6 KiB
license, language, library_name, pipeline_tag, tags
| license | language | library_name | pipeline_tag | tags | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
transformers | text-generation |
|
slm-125m-base
A 125M-parameter Llama-architecture small language model pretrained from scratch on a legal/financial corpus. This is a base (pretrained) model — it is not instruction- or chat-tuned.
Model details
| Architecture | Llama (via transformers.LlamaForCausalLM) |
| Parameters | 125.8M (tied embeddings) |
| Vocab | 16,384 (byte-level BPE trained on this corpus) |
| Layers / hidden / heads | 12 / 768 / 12 (head dim 64, MHA) |
| Context length | 1,024 |
| Positional | RoPE (θ=10,000) |
| Norm / activation | RMSNorm (1e-5) / SwiGLU (silu) |
| Precision | bf16 training, weights saved fp32 |
Training data (~2.04B tokens, "legal-first" mix)
Streamed, cleaned, deduplicated (MinHash LSH + exact), and decontaminated against CaseHOLD / LexGLUE before tokenization. Realized token mix:
| Source | Share | HF dataset |
|---|---|---|
| US case law | ~35% | HFforLegal/case-law (split us) |
| SEC filings | ~42% | PleIAs/SEC |
| Educational web | ~23% | HuggingFaceFW/fineweb-edu (sample-10BT) |
Training
- 2 epochs (7,778 steps) on 8×H100 (DDP,
torch.compile, SDPA/flash attention) - Global batch 524,288 tokens; AdamW (β=0.9/0.95, wd 0.1, clip 1.0)
- LR 6e-4 → 6e-5 cosine, 200M-token linear warmup; seed 1337
- Throughput ~3.19M tok/s @ ~30% MFU
Evaluation
Held-out validation perplexity: 9.13 (loss 2.211, full 1% held-out split, 20,581,737 tokens / 20,119 packed 1024-token windows).
Validation loss over training (subset eval): 2.796 → 2.232 (steps 1000 → 7778).
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tok = AutoTokenizer.from_pretrained("s2211252/slm-125m-base")
model = AutoModelForCausalLM.from_pretrained("s2211252/slm-125m-base", torch_dtype=torch.bfloat16)
prompt = "Pursuant to the terms of this Agreement, the parties"
ids = tok(prompt, return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=120, do_sample=True, top_k=50, top_p=0.95, temperature=0.8)
print(tok.decode(out[0], skip_special_tokens=True))
Limitations
Small base model, English only, 1,024-token context. Trained on legal/financial + web text; it is not instruction-tuned and can produce inaccurate or fabricated legal/financial statements. Not for legal advice or production decisions without review. Domain contamination against CaseHOLD/LexGLUE was filtered, but standard LM caveats apply.