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Model: ppanja/slm-125m-base
Source: Original Platform

README.md

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+---
+language: en
+license: apache-2.0
+tags:
+  - causal-lm
+  - llama
+  - legal
+  - financial
+  - from-scratch
+library_name: transformers
+---
+
+# slm-125m
+
+125M-parameter causal language model trained from scratch on a legal/financial corpus.
+
+## Model
+
+| | |
+|---|---|
+| Architecture | Llama-style (SwiGLU, RoPE, RMSNorm) |
+| Parameters | ~125.8M |
+| Layers / dim / heads | 12 / 768 / 12 |
+| Context | 1024 tokens |
+| Vocab | 16384 (byte-level BPE) |
+
+## Training data
+
+| Source | Role |
+|--------|------|
+| HFforLegal/case-law | US case law |
+| PleIAs/SEC | SEC filings |
+| HuggingFaceFW/fineweb-edu | General educational web text |
+
+Packed train tokens: **unknown** | val tokens: **unknown**
+
+Eval benchmarks (LexGLUE, CaseHOLD) were held out during corpus construction.
+
+## Training run
+
+| Hyperparameter | Value |
+|----------------|-------|
+| Global batch (tokens) | 524,288 |
+| LR / min LR | 0.0006 / 6e-05 |
+| Warmup tokens | 200M |
+| Weight decay | 0.1 |
+
+Last logged train loss: **n/a** | val loss: **n/a**
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("ppanja/slm-125m-base")
+model = AutoModelForCausalLM.from_pretrained("ppanja/slm-125m-base")
+inputs = tok("The plaintiff shall bear the burden of proof", return_tensors="pt")
+out = model.generate(**inputs, max_new_tokens=64)
+print(tok.decode(out[0]))
+```
+
+Chat special tokens (`<|user|>`, `<|assistant|>`, `<|system|>`) are in the vocabulary for future instruction tuning.