69 lines
1.9 KiB
Markdown
69 lines
1.9 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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tags:
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- legal
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- finance
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- llama
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pipeline_tag: text-generation
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---
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# slm-125m-base
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A 126M-parameter Llama-architecture base model pretrained **from
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scratch** on US case law, SEC filings, and educational web text. Trained on
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8xH100 for ~28 minutes at a total project cost of roughly $18.
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This is a **base model**. It has had no instruction tuning, no RLHF, and no
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safety alignment. It completes text; it does not follow instructions.
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## Architecture
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| Parameters | 125,847,552 |
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| Layers / hidden / heads | 12 / 768 / 12 |
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| Context length | 1024 |
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| Vocab | 16384 (byte-level BPE trained on this corpus) |
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| Tied embeddings | True |
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## Training data
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1.92B tokens, ~76% legal:
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| Source | Tokens | Share |
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| `PleIAs/SEC` | 819M | 43% |
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| `HFforLegal/case-law` | 639M | 33% |
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| `HuggingFaceFW/fineweb-edu` | 463M | 24% |
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Pipeline: deterministic rule-based cleaning (line filters, boilerplate
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stripping, repetition and language gates, plus a dictionary-based OCR gate on
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case law) then MinHash near-dedup, exact dedup, and 13-gram decontamination
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against the CaseHOLD evaluation set.
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## Training
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2 epochs (3.84B tokens seen), AdamW, cosine schedule to a 6e-05 cooldown,
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bf16, sequence length 1024, global batch 524,288 tokens.
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## Results
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| Metric | Value |
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|---|---|
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| Val perplexity (held-out 1%) | 9.07 |
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| CaseHOLD (zero-shot, 5-way) | 19.5% |
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CaseHOLD is measured on data excluded from training by 13-gram
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decontamination, so it is not inflated by leakage. Expect near-chance
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zero-shot performance: a 125M base model with no task tuning is not
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competitive on multiple-choice legal reasoning, and this number is a baseline
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for fine-tuning, not a capability claim.
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## Limitations
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Small, base-only, English-only, 1024-token context. Case-law inputs contain
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OCR noise that survives filtering. **Not legal or financial advice** and not
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suitable for any use where an incorrect statement of law carries consequences.
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