From d3961d408b19e35dd4e6b185c38bd8e28443403a Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Fri, 7 Aug 2026 07:53:44 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: Bias-variance-tradeoff/slm-125m-base Source: Original Platform --- .gitattributes | 35 + README.md | 152 + config.json | 29 + eval.json | 6 + generation_config.json | 6 + model.safetensors | 3 + special_tokens_map.json | 35 + tokenizer.json | 80966 ++++++++++++++++++++++++++++++++++++++ tokenizer_config.json | 72 + train_summary.json | 8 + 10 files changed, 81312 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 eval.json create mode 100644 generation_config.json create mode 100644 model.safetensors create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 train_summary.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..325dcbf --- /dev/null +++ b/README.md @@ -0,0 +1,152 @@ +--- +license: apache-2.0 +language: +- en +library_name: transformers +pipeline_tag: text-generation +tags: +- llama +- legal +- finance +- small-language-model +- pretrained-from-scratch +- vizuara +datasets: +- HFforLegal/case-law +- PleIAs/SEC +- HuggingFaceFW/fineweb-edu +metrics: +- perplexity +--- + +# SLM-125M-base + +A **125.8M-parameter Llama-style language model pretrained from random weights** on a +legal + financial corpus. It is a **base completer** (next-token prediction only), not +an instruction-tuned chatbot — give it the start of a sentence and it continues in the +legal/financial register. + +Built end-to-end on [Modal](https://modal.com) for the **Vizuara "SLM from scratch" +workshop**, replicating the reference pipeline at +[Vizuara-AI-Lab/slm-125m-from-scratch](https://github.com/Vizuara-AI-Lab/slm-125m-from-scratch). + +- 🕹️ **Live demo:** https://slm-125m-site.vercel.app +- ⚡ **Inference API:** `https://dharsourav03--slm-125m-inference-web.modal.run` (`/generate`) + +| | | +|---|---| +| **Parameters** | 125,847,552 (~125.8M, tied embeddings) | +| **Validation perplexity** | **10.87** (held-out 1% split, ~22.0M tokens) | +| **Train tokens** | 2.18B (1 epoch) | +| **Vocab / context** | 16,384 byte-level BPE / 1,024 tokens | + +## Intended use + +Prompt it with the **opening of a sentence** and it completes the thought in a legal or +financial style. It is a demonstration of the full from-scratch pipeline (data → +tokenizer → pretraining), not a production model. + +**Not intended for:** factual question answering, chat, instruction following, or any +high-stakes legal/financial decision-making. At 125M parameters it holds very little +factual knowledge and will confidently produce plausible-sounding but incorrect content. + +## Model architecture + +Maps 1:1 to `transformers.LlamaConfig`. + +| Component | Value | +|---|---| +| Architecture | Llama-style decoder-only transformer | +| Layers | 12 | +| Hidden size | 768 | +| Attention heads | 12 (head dim 64), MHA (kv-heads = 12) | +| MLP | SwiGLU, inner dim 3072 | +| Normalization | RMSNorm (pre-norm), eps 1e-5 | +| Positional encoding | RoPE (theta 10000) | +| Context length | 1024 tokens | +| Vocabulary | 16,384 (byte-level BPE), tied embeddings | +| Attention bias | none | + +## Training data + +Streamed from HuggingFace, cleaned, deduplicated, and decontaminated. Realized mix +(by real tokens): + +| Source | Content | Share | +|---|---|---| +| [HFforLegal/case-law](https://huggingface.co/datasets/HFforLegal/case-law) | US court opinions | ~33% | +| [PleIAs/SEC](https://huggingface.co/datasets/PleIAs/SEC) | SEC filings (10-K, etc.) | ~39% | +| [HuggingFaceFW/fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) | Educational web text | ~28% | + +**Pipeline:** stream → deterministic rule-based cleaning (line filters, boilerplate +strip, repetition/language/OCR gates) → exact + MinHash near-dedup → 13-gram +decontamination against CaseHOLD / LexGLUE eval sets → 16K byte-level BPE tokenizer → +pack into 1024-token windows (99/1 train/val split). Final corpus: **2.18B train + 22.0M +val tokens**. + +## Training procedure + +| Hyperparameter | Value | +|---|---| +| Objective | Next-token cross-entropy (causal LM) | +| Epochs | 1 (2.18B tokens seen) | +| Optimizer | AdamW, betas (0.9, 0.95), weight decay 0.1 | +| Learning rate | 6e-4 → 6e-5, cosine decay | +| Warmup | 200M tokens | +| Gradient clip | 1.0 | +| Global batch | 524,288 tokens/step (micro-batch 32 × seq 1024, grad-accum 2) | +| Precision | bf16 | +| Hardware | 8×H100 (single-node DDP), ~24 min | + +> The reference workshop trains ~5 epochs (→ val perplexity 8.50). This checkpoint is a +> **single-epoch** run (val perplexity 10.87); more epochs over the same fixed corpus +> lower perplexity further. + +## Evaluation + +Held-out validation perplexity is `exp(mean token-level cross-entropy)` over the 1% +val split (21,505 windows, ~22.0M tokens): + +**Validation perplexity: 10.87** (val loss 2.3856). + +## How to use + +The model uses custom special tokens; prepending `<|bos|>` matches how it was served and +gives the best continuations. + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +repo = "Bias-variance-tradeoff/slm-125m-base" +tok = AutoTokenizer.from_pretrained(repo) +model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.float32).eval() + +prompt = "The plaintiff alleges that the defendant" +bos = tok.convert_tokens_to_ids("<|bos|>") +eos = tok.convert_tokens_to_ids("<|eos|>") +ids = torch.tensor([[bos] + tok.encode(prompt, add_special_tokens=False)]) + +out = model.generate( + ids, max_new_tokens=90, min_new_tokens=40, do_sample=True, + temperature=0.8, top_k=50, top_p=0.95, repetition_penalty=1.3, + eos_token_id=eos, pad_token_id=eos, +) +print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)) +``` + +## Limitations and bias + +- **Base model, not aligned** — no instruction tuning, no RLHF, no safety filtering. +- **Not a knowledge base** — a 125M model stores only a few tens of MB of usable + knowledge; it fabricates citations, statutes, and figures. Do not rely on any factual + claim it makes. +- **Domain-skewed** — trained ~72% on US legal + SEC text, so it defaults to that + register and reflects the biases of those corpora (US-centric law, corporate finance). +- **English only.** + +## Citation / attribution + +Trained from scratch as part of the **Vizuara AI Labs "SLM from scratch" workshop**, +following the pipeline in +[Vizuara-AI-Lab/slm-125m-from-scratch](https://github.com/Vizuara-AI-Lab/slm-125m-from-scratch). diff --git a/config.json b/config.json new file mode 100644 index 0000000..30291b8 --- /dev/null +++ b/config.json @@ -0,0 +1,29 @@ +{ + "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, + 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"Ġblo": 11599, + "ĠUnless": 11600, + "cence": 11601, + "Ġsurrender": 11602, + "Ġ190": 11603, + "ordan": 11604, + "Ġsupreme": 11605, + "ĠDetective": 11606, + "Ġmolecules": 11607, + "itively": 11608, + "ĠTHIS": 11609, + "ĠFil": 11610, + "Ġweakness": 11611, + "Ġrationale": 11612, + "Ġ1948": 11613, + "mitting": 11614, + "Ġfighting": 11615, + "Ġblank": 11616, + "ĠZealand": 11617, + "Ġfitness": 11618, + "ĠSTOCKHOLDER": 11619, + "Ġembra": 11620, + "ourage": 11621, + "Ġfly": 11622, + "ĠEU": 11623, + "ĠProceeds": 11624, + "ICO": 11625, + "Ġenhancements": 11626, + "Ġaqu": 11627, + "Ġmanip": 11628, + "ĠEL": 11629, + "Ġdin": 11630, + "ĠHum": 11631, + "Ġmyth": 11632, + "Ġ129": 11633, + "Ġliquor": 11634, + "Ġprecisely": 11635, + "veloper": 11636, + "ĠDallas": 11637, + "Ġperforms": 11638, + "Ġmanages": 11639, + "range": 11640, + "icus": 11641, + "chnology": 11642, + "Ġrelying": 11643, + "375": 11644, + "ĠCf": 11645, + "Ġconcludes": 11646, + "Ġwound": 11647, + "Ġfilter": 11648, + "Ġtends": 11649, + 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"Ġpreponder": 11803, + "ĠImpairment": 11804, + "ĠApplication": 11805, + "ĠKenneth": 11806, + "Ġreferring": 11807, + "IGH": 11808, + "ĠSon": 11809, + "Ġdirecting": 11810, + "ĠAmer": 11811, + "Ġpu": 11812, + "ĠGolden": 11813, + "uther": 11814, + "ĠMerr": 11815, + "Ġbones": 11816, + "Äģ": 11817, + "VEL": 11818, + "Ġbehaviour": 11819, + "ĠPortland": 11820, + "Ġforfeiture": 11821, + "INE": 11822, + "Ġinterlocutory": 11823, + "Ġtight": 11824, + "Ġassociate": 11825, + "Ġwww": 11826, + "Ġaggressive": 11827, + "'m": 11828, + "ĠEvent": 11829, + "ĠCore": 11830, + "ĠRetail": 11831, + "INESS": 11832, + "Ġwriter": 11833, + "ĠSoviet": 11834, + "Ġ138": 11835, + "Ġintangibles": 11836, + "Ġgasoline": 11837, + "2006": 11838, + "Ġsusceptible": 11839, + "benef": 11840, + "citation": 11841, + "ĠHig": 11842, + "Ġcollaborative": 11843, + "Ġtrail": 11844, + "Ġ342": 11845, + "Ġstomach": 11846, + "ĠStudy": 11847, + "Ġprecluded": 11848, + "Ġretroactive": 11849, + "ĠALL": 11850, + "ĠHom": 11851, + "ĠBall": 11852, 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"Ġshel": 11954, + "ĠMexican": 11955, + "odies": 11956, + "Ġlisting": 11957, + "Ġsilver": 11958, + "Ġsweet": 11959, + "Ġax": 11960, + "Ġanalyzed": 11961, + "Ġcryst": 11962, + "Ġundisc": 11963, + "hma": 11964, + "Ġsilent": 11965, + "Ġconstantly": 11966, + "ĠErn": 11967, + "Ġcolleagues": 11968, + "Ġdeeds": 11969, + "ĠGaming": 11970, + "ĠINVEST": 11971, + "ught": 11972, + "Ġfeat": 11973, + "Ġtube": 11974, + "ĠCro": 11975, + "etitive": 11976, + "Ġconstructive": 11977, + "Ġgathering": 11978, + "Ġpreferences": 11979, + "ĠMort": 11980, + "ĠTravel": 11981, + "advant": 11982, + "verted": 11983, + "ĠDetroit": 11984, + "Ġgradually": 11985, + "leted": 11986, + "atible": 11987, + "Ġspon": 11988, + "issues": 11989, + "Ġ302": 11990, + "kee": 11991, + "Ġforests": 11992, + "Ġboat": 11993, + "Ġscore": 11994, + "feas": 11995, + "Ġfairness": 11996, + "rawn": 11997, + "Ġspecify": 11998, + "Ġlogical": 11999, + "ĠRos": 12000, + "Ġnationwide": 12001, + "ĠJUD": 12002, + "Ġinterior": 12003, + "Ġfalling": 12004, 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