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

README.md

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