初始化项目，由ModelHub XC社区提供模型

Model: VigneshwarKandhaiya/legal-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:
+- legal
+- finance
+- llama
+- pretraining
+- small-language-model
+datasets:
+- HFforLegal/case-law
+- PleIAs/SEC
+- HuggingFaceFW/fineweb-edu
+---
+
+# legal-slm-125m-base
+
+A 125M-parameter Llama-architecture small language model pretrained **from
+scratch** on a legal/financial corpus. Base (not instruction-tuned) model.
+
+## Results
+- **Validation perplexity: 10.67** (val_loss 2.3674), 1 epoch over 1.97B tokens.
+
+## Model
+- 125,847,552 params — 12 layers / 768 hidden / 12 heads, context 1024,
+  vocab 16,384 (custom byte-level BPE), tied embeddings, SwiGLU, RoPE.
+
+## Training data (~76% legal)
+- `HFforLegal/case-law` (US court opinions) — ~36%
+- `PleIAs/SEC` (SEC filings) — ~40%
+- `HuggingFaceFW/fineweb-edu` (educational web) — ~24%
+
+Pipeline: deterministic cleaning (OCR gate, boilerplate/repetition/language
+filters) → MinHash near-dup + exact-dup removal → 13-gram decontamination
+against CaseHOLD/LexGLUE (so eval is uncontaminated) → 16K byte-level BPE →
+packed 1024-token windows.
+
+## Training
+- 1 epoch, 3,753 steps, global batch 524,288 tokens, 2x H100 (DDP, bf16,
+  torch.compile). AdamW (0.9/0.95, wd 0.1 on 2D params), grad-clip 1.0.
+- LR: warmup to 6e-4 over 200M tokens, cosine to 6e-5 annealed over 1 epoch.
+
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("VigneshwarKandhaiya/legal-slm-125m-base")
+model = AutoModelForCausalLM.from_pretrained("VigneshwarKandhaiya/legal-slm-125m-base")
+ids = tok("The plaintiff shall bear the burden of", return_tensors="pt").input_ids
+print(tok.decode(model.generate(ids, max_new_tokens=40)[0], skip_special_tokens=True))
+```
+
+## Limitations
+Base model, English only, 125M params — expect fluent domain-styled text, not
+reliable facts or instruction-following. Not legal or financial advice.