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Model: Sudhanshu1985/slm-125m-base Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- llama
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- legal
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- finance
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- small-language-model
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- pretrained-from-scratch
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- causal-lm
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datasets:
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- HFforLegal/case-law
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- PleIAs/SEC
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- HuggingFaceFW/fineweb-edu
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---
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# SLM-125M-base
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A **125.8M-parameter, Llama-style language model pretrained from scratch** on a
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cleaned, deduplicated, and decontaminated legal + financial corpus. Give it the
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start of a sentence and it continues in the legal/financial register.
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This is a **base completer**, not a chat model. It was trained on next-token
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prediction only, so it *continues* text rather than answering questions.
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## Model details
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| Parameters | 125,847,552 (~125.8M) |
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| Architecture | Llama-style (`LlamaForCausalLM`) |
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| Layers | 12 |
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| Hidden size | 768 |
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| Attention heads | 12 (head dim 64), MHA (12 KV heads) |
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| MLP | SwiGLU, inner 3072 |
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| Normalization | RMSNorm, pre-norm |
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| Positional | RoPE (theta 10000) |
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| Context length | 1024 |
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| Vocab | 16,384 (byte-level BPE, trained from scratch) |
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| Embeddings | tied (input = output) |
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| Precision | bf16 |
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## Training data
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A **~2.04B-token** corpus built from three public, ungated sources, streamed and
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filtered through a deterministic cleaning + dedup + decontamination pipeline:
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| Source | Content | Share |
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|---|---|---|
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| [`HFforLegal/case-law`](https://huggingface.co/datasets/HFforLegal/case-law) | US court opinions | ~40% |
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| [`PleIAs/SEC`](https://huggingface.co/datasets/PleIAs/SEC) | SEC filings (10-K, etc.) | ~40% |
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| [`HuggingFaceFW/fineweb-edu`](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) | Educational web text | ~20% |
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The corpus was decontaminated against CaseHOLD / LexGLUE (13-gram overlap removed)
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and deduplicated (exact + MinHash/LSH near-duplicate removal).
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## Training recipe
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| Knob | Value |
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|---|---|
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| Objective | next-token cross-entropy |
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| Hardware | 8× H100 (single-node DDP) |
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| Epochs | 1 (~2.04B tokens seen) |
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| Optimizer | AdamW, betas (0.9, 0.95), weight decay 0.1 |
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| LR | 6e-4 → 6e-5, cosine decay, 200M-token warmup |
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| Global batch | ~524,288 tokens/step |
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| Grad clip | 1.0 |
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| Steps | 3,889 |
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## Results
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**Held-out validation perplexity: 11.01** (val loss 2.42) on a 1% held-out split
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(~20.6M tokens). Perplexity was still descending at the end of the single epoch
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(step 2000 → 12.54, step 3000 → 11.27, final → 11.01), so additional epochs would
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lower it further. Total compute cost to train: **~$11**.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("Sudhanshu1985/slm-125m-base")
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model = AutoModelForCausalLM.from_pretrained("Sudhanshu1985/slm-125m-base")
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prompt = "The plaintiff alleges that the defendant"
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inputs = tok(prompt, return_tensors="pt")
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out = model.generate(
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**inputs,
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max_new_tokens=80,
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min_new_tokens=40,
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do_sample=True,
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temperature=0.8,
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top_p=0.95,
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top_k=40,
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repetition_penalty=1.3,
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)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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### Example completions
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- *"The plaintiff alleges that the defendant"* → "breached its contract of employment... In order to prove fraud, a party must show: (1) The existence of a confidential relationship; (2) the absence or violation by one party of any duty owed..."
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- *"Pursuant to the terms of this Agreement,"* → "the parties have agreed as follows: 1. ...all disputes... shall be subject to the jurisdiction of said Court..."
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- *"The Company's net revenues for the fiscal year"* → "ended September 30, 1996 were $305.1 million. The operating loss in fiscal 1995 was primarily attributable to..."
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## Limitations
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- **Base completer, not a chatbot.** It continues text; it does not follow
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instructions or answer questions.
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- **Does not know facts.** At 125M parameters a model holds only a small amount of
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usable knowledge; grounded facts would require retrieval (RAG).
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- **Domain-biased.** It speaks the legal register fluently; non-legal prompts drift
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toward that register.
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- Trained on US legal/financial + educational web text; may reflect biases in those
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sources. Not legal or financial advice.
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## Provenance
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Pretrained from random weights (nothing fine-tuned). Pipeline: stream 3 datasets →
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rule-based cleaning → dedup + decontaminate → 16K byte-level BPE → pack 1024-token
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windows → pretrain on 8× H100. Based on the Vizuara AI Labs
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[slm-125m-from-scratch](https://github.com/Vizuara-AI-Lab/slm-125m-from-scratch) recipe.
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