初始化项目，由ModelHub XC社区提供模型

Model: srinivasch87/slm125live-base
Source: Original Platform

README.md

@@ -0,0 +1,92 @@
+---
+library_name: transformers
+tags:
+  - text-generation
+  - legal
+  - finance
+  - pretraining
+  - from-scratch
+pipeline_tag: text-generation
+---
+
+# SLM125 — a 125M-parameter legal/financial base language model, trained from scratch
+
+**SLM125** is a small (~125.8M parameter), decoder-only transformer pretrained from
+scratch on a legal- and finance-heavy corpus. It is a **base** model — it completes
+text, it is not instruction-tuned or chat-tuned. Give it a sentence to continue
+("The plaintiff argued that ..."), not a question.
+
+🔗 **Try it live:** [SLM125 Playground](https://web-roan-chi-97.vercel.app)
+
+## Model Details
+
+- **Architecture:** Llama-style decoder-only transformer (RoPE, RMSNorm, SwiGLU MLP, tied embeddings)
+- **Parameters:** ~125.8M
+- **Hidden size:** 768
+- **Layers:** 12
+- **Attention heads:** 12 (head dim 64, standard multi-head attention — no GQA)
+- **Intermediate (MLP) size:** 3,072
+- **Context length:** 1,024 tokens
+- **Vocabulary:** 16,384 tokens (custom-trained tokenizer)
+- **RoPE theta:** 10,000
+- **Precision:** trained and served in fp32/bf16 on Modal
+
+## Training Data
+
+Pretrained on a cleaned, deduplicated, decontaminated corpus of **~2.19B tokens**,
+mixed "legal-first" from three public, streamed HuggingFace datasets:
+
+| Source | Dataset | Share | What it is |
+|---|---|---|---|
+| case-law | [`HFforLegal/case-law`](https://huggingface.co/datasets/HFforLegal/case-law) (`us` config) | ~39% (~863M tokens) | US court opinions (scanned; some OCR noise) |
+| sec | [`PleIAs/SEC`](https://huggingface.co/datasets/PleIAs/SEC) | ~39% (~861M tokens) | SEC filings (10-K, etc.), born-digital |
+| fineweb-edu | [`HuggingFaceFW/fineweb-edu`](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) (`sample-10BT`) | ~21% (~465M tokens) | General educational web text, added as fluency filler |
+
+The two legal sources were taken in full (they cap out around 2B tokens combined);
+a smaller web slice was added on top, landing at roughly a 40/40/20 split — about
+78% legal/financial text overall, not the originally-planned 70/20/10.
+
+## How to Get Started
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "srinivasch87/slm125live-base"
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo)
+
+prompt = "The plaintiff argued that"
+inputs = tokenizer(prompt, return_tensors="pt")
+output = model.generate(
+    **inputs,
+    max_new_tokens=128,
+    do_sample=True,
+    temperature=0.8,
+    top_p=0.95,
+    top_k=50,
+)
+print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+## Intended Use & Limitations
+
+- This is a **base completion model**, not an instruction-following assistant.
+  It will not reliably answer questions, follow chat-style instructions, or
+  refuse unsafe requests — prompt it with the start of a sentence or document.
+- At 125M parameters and ~2.2B training tokens, factual accuracy, coherence over
+  long spans, and reasoning are limited. Treat all output as unverified
+  autocomplete, not legal, financial, or professional advice.
+- Training data skews heavily toward US case law and SEC filings, so the model's
+  style and any biases reflect that domain (and the OCR noise present in the
+  scanned case-law source).
+- License is not specified for this model; the underlying training datasets each
+  carry their own terms — check the dataset cards linked above before
+  redistributing outputs.
+
+## Training & Serving Infrastructure
+
+Built end-to-end on [Modal](https://modal.com): data cleaning, deduplication,
+tokenizer training, and pretraining all ran as fanned-out CPU/GPU Modal functions
+against a shared Modal Volume. Inference is served from a Modal web endpoint with
+token-streaming generation, behind a Next.js frontend deployed on Vercel (link
+above).