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Model: Sudhanshu1985/slm-125m-base
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- llama
+- legal
+- finance
+- small-language-model
+- pretrained-from-scratch
+- causal-lm
+datasets:
+- HFforLegal/case-law
+- PleIAs/SEC
+- HuggingFaceFW/fineweb-edu
+---
+
+# SLM-125M-base
+
+A **125.8M-parameter, Llama-style language model pretrained from scratch** on a
+cleaned, deduplicated, and decontaminated legal + financial corpus. Give it the
+start of a sentence and it continues in the legal/financial register.
+
+This is a **base completer**, not a chat model. It was trained on next-token
+prediction only, so it *continues* text rather than answering questions.
+
+## Model details
+
+| | |
+|---|---|
+| Parameters | 125,847,552 (~125.8M) |
+| Architecture | Llama-style (`LlamaForCausalLM`) |
+| Layers | 12 |
+| Hidden size | 768 |
+| Attention heads | 12 (head dim 64), MHA (12 KV heads) |
+| MLP | SwiGLU, inner 3072 |
+| Normalization | RMSNorm, pre-norm |
+| Positional | RoPE (theta 10000) |
+| Context length | 1024 |
+| Vocab | 16,384 (byte-level BPE, trained from scratch) |
+| Embeddings | tied (input = output) |
+| Precision | bf16 |
+
+## Training data
+
+A **~2.04B-token** corpus built from three public, ungated sources, streamed and
+filtered through a deterministic cleaning + dedup + decontamination pipeline:
+
+| Source | Content | Share |
+|---|---|---|
+| [`HFforLegal/case-law`](https://huggingface.co/datasets/HFforLegal/case-law) | US court opinions | ~40% |
+| [`PleIAs/SEC`](https://huggingface.co/datasets/PleIAs/SEC) | SEC filings (10-K, etc.) | ~40% |
+| [`HuggingFaceFW/fineweb-edu`](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) | Educational web text | ~20% |
+
+The corpus was decontaminated against CaseHOLD / LexGLUE (13-gram overlap removed)
+and deduplicated (exact + MinHash/LSH near-duplicate removal).
+
+## Training recipe
+
+| Knob | Value |
+|---|---|
+| Objective | next-token cross-entropy |
+| Hardware | 8× H100 (single-node DDP) |
+| Epochs | 1 (~2.04B tokens seen) |
+| Optimizer | AdamW, betas (0.9, 0.95), weight decay 0.1 |
+| LR | 6e-4 → 6e-5, cosine decay, 200M-token warmup |
+| Global batch | ~524,288 tokens/step |
+| Grad clip | 1.0 |
+| Steps | 3,889 |
+
+## Results
+
+**Held-out validation perplexity: 11.01** (val loss 2.42) on a 1% held-out split
+(~20.6M tokens). Perplexity was still descending at the end of the single epoch
+(step 2000 → 12.54, step 3000 → 11.27, final → 11.01), so additional epochs would
+lower it further. Total compute cost to train: **~$11**.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("Sudhanshu1985/slm-125m-base")
+model = AutoModelForCausalLM.from_pretrained("Sudhanshu1985/slm-125m-base")
+
+prompt = "The plaintiff alleges that the defendant"
+inputs = tok(prompt, return_tensors="pt")
+out = model.generate(
+    **inputs,
+    max_new_tokens=80,
+    min_new_tokens=40,
+    do_sample=True,
+    temperature=0.8,
+    top_p=0.95,
+    top_k=40,
+    repetition_penalty=1.3,
+)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+
+### Example completions
+
+- *"The plaintiff alleges that the defendant"* → "breached its contract of employment... In order to prove fraud, a party must show: (1) The existence of a confidential relationship; (2) the absence or violation by one party of any duty owed..."
+- *"Pursuant to the terms of this Agreement,"* → "the parties have agreed as follows: 1. ...all disputes... shall be subject to the jurisdiction of said Court..."
+- *"The Company's net revenues for the fiscal year"* → "ended September 30, 1996 were $305.1 million. The operating loss in fiscal 1995 was primarily attributable to..."
+
+## Limitations
+
+- **Base completer, not a chatbot.** It continues text; it does not follow
+  instructions or answer questions.
+- **Does not know facts.** At 125M parameters a model holds only a small amount of
+  usable knowledge; grounded facts would require retrieval (RAG).
+- **Domain-biased.** It speaks the legal register fluently; non-legal prompts drift
+  toward that register.
+- Trained on US legal/financial + educational web text; may reflect biases in those
+  sources. Not legal or financial advice.
+
+## Provenance
+
+Pretrained from random weights (nothing fine-tuned). Pipeline: stream 3 datasets →
+rule-based cleaning → dedup + decontaminate → 16K byte-level BPE → pack 1024-token
+windows → pretrain on 8× H100. Based on the Vizuara AI Labs
+[slm-125m-from-scratch](https://github.com/Vizuara-AI-Lab/slm-125m-from-scratch) recipe.

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 1024,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 12,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "use_cache": true,
+  "vocab_size": 16384
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.46.3"
+}

model.safetensors

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+size 251708904

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|user|>",
+    "<|assistant|>",
+    "<|system|>"
+  ],
+  "bos_token": "<|bos|>",
+  "eos_token": "<|eos|>",
+  "pad_token": "<|pad|>",
+  "unk_token": "<|unk|>"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<|bos|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<|eos|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<|pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<|unk|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "5": {
+      "content": "<|assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "6": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|user|>",
+    "<|assistant|>",
+    "<|system|>"
+  ],
+  "bos_token": "<|bos|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|eos|>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<|pad|>",
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "<|unk|>"
+}