初始化项目，由ModelHub XC社区提供模型

Model: MaliosDark/Nexus-Erebus-50M
Source: Original Platform

README.md

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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- en
+tags:
+- small-language-model
+- slm
+- from-scratch
+- tiny
+- nexus-erebus
+- llama
+- arithmetic
+---
+
+![Nexus-Erebus-50M](./nexus50m.png)
+
+# Nexus-Erebus-50M
+
+**Nexus-Erebus-50M** is a compact ~54M-parameter language model trained from scratch by **Ideoa Labs**,
+combining strong commonsense reasoning with genuine integer arithmetic ability at tiny scale.
+
+## Model details
+
+| | |
+|---|---|
+| Parameters | ~54.1M |
+| Architecture | Llama-style decoder |
+| Hidden size | 512 |
+| Layers | 9 |
+| Attention heads | 8 |
+| Vocab size | 32,000 (custom digit-aware tokenizer) |
+| Context length | 1,024 |
+| Precision | bfloat16 |
+
+The tokenizer keeps digits atomic rather than merging them into BPE units, which preserves the
+positional structure that integer arithmetic depends on.
+
+## Benchmarks
+
+Measured with `lm-eval-harness`, 0-shot, `acc_norm`, on the **full test sets** (no subsampling).
+ArithMark-2 is scored on its full 2500 items with the public evaluation script.
+
+| Task | Items | Nexus-Erebus-50M |
+|---|---:|---:|
+| ARC-easy | 2,376 | 44.40 |
+| ARC-challenge | 1,172 | 22.70 |
+| HellaSwag | 10,042 | 27.05 |
+| PIQA | 1,838 | 58.27 |
+| **ArithMark-2** | 2,500 | **52.48** |
+| **Average** | | **40.98** |
+
+### Against the sub-100M field
+
+Published Open SLM Leaderboard values, same five tasks, same full-set protocol.
+
+| Model | Params | Average |
+|---|---:|---:|
+| **Nexus-Erebus-50M** | **54M** | **40.98** |
+| Atom 2.7M | 3M | 40.43 |
+| Supra-1.5-50M-base-exp | 52M | 39.00 |
+| Isabel-50M | 54M | 38.74 |
+| Supra-50M-Base | 52M | 38.45 |
+| Archaea-74M-V1.1 | 74M | 37.96 |
+
+It leads the sub-100M class on ARC-easy and PIQA, and its ArithMark-2 score of 52.48 is the second
+highest in that class.
+
+## Training
+
+Trained from scratch with a custom digit-aware 32k tokenizer, then refined on a curated mix of
+educational, science, commonsense and reading-comprehension data, plus a large synthetic integer
+arithmetic set covering addition, subtraction, multiplication, exact division, mixed multi-operator
+expressions and parenthesised expressions. No benchmark test items were used at any stage.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("MaliosDark/Nexus-Erebus-50M")
+model = AutoModelForCausalLM.from_pretrained("MaliosDark/Nexus-Erebus-50M")
+
+prompt = "16 + 4 * 3 ="
+print(tok.decode(model.generate(**tok(prompt, return_tensors="pt"), max_new_tokens=6)[0]))
+```
+
+## Example outputs
+
+Real, unedited outputs from this checkpoint.
+
+| Prompt | Output |
+|---|---|
+| `Question: What force pulls objects toward the Earth?` | gravity. |
+| `Question: What gas do humans need to breathe to survive?` | oxygen. |
+| `Question: What do we call the process by which plants make food?` | photosynthesis. |
+
+## License
+
+Apache-2.0.
+
+Built by Ideoa Labs.