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Model: jonam-ai/slm-125m-base
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama
+- legal
+- financial
+- small-language-model
+- pretrained
+---
+
+# 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
+
+```python
+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.