58 lines
1.8 KiB
Markdown
58 lines
1.8 KiB
Markdown
---
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license: apache-2.0
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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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---
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# shivamfet/slm-125m-base
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A ~125M-parameter Llama-architecture language model pretrained from scratch on a
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legal- and finance-heavy corpus (U.S. case law, SEC filings, and a slice of
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FineWeb-Edu). Built as a compact domain SLM; not instruction-tuned.
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**🌐 Live demo:** https://slm-125m-shivamk.vercel.app
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## Model details
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| Parameters | ~125.8M (tied embeddings) |
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| Architecture | Llama (12 layers, 768 hidden, 12 heads) |
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| Vocab | 16,384 (byte-level BPE, trained on-corpus) |
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| Context length | 1024 |
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| Precision | bf16 autocast (fp32 master weights) |
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## Training
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- **Corpus:** ~2.4B unique tokens (case-law / SEC / FineWeb-Edu), deduplicated
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(MinHash LSH) and decontaminated against CaseHOLD / LexGLUE eval sets.
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- **Schedule:** 4 epochs (~8.15B tokens seen) on 8xH100, cosine LR (6e-4 -> 6e-5),
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token-based warmup, fused AdamW.
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- **Result:** validation perplexity 16.0 -> 8.35, monotonic, no divergence.
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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("shivamfet/slm-125m-base")
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model = AutoModelForCausalLM.from_pretrained("shivamfet/slm-125m-base")
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ids = tok("The plaintiff shall bear the burden of proving",
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return_tensors="pt", return_token_type_ids=False)
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out = model.generate(**ids, max_new_tokens=80, do_sample=True,
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top_k=50, temperature=0.8, pad_token_id=tok.pad_token_id)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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## Limitations
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Base model only — no instruction tuning or RLHF. It can produce plausible-sounding
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but incorrect legal/financial text and must not be relied on for legal or financial
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advice. Reflects biases in its public training sources.
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