初始化项目，由ModelHub XC社区提供模型

Model: finnianx/michel-nano
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- mergekit
+- merge
+datasets:
+- HuggingFaceFW/fineweb-edu
+- epfml/FineWeb2-HQ
+- HuggingFaceTB/cosmopedia
+language:
+- en
+---
+# michel-nano
+
+michel-nano is an ultra-tiny ~6 million parameter base language model trained on 1.14 billion tokens. It was created by merging two intermediate training checkpoints using [mergekit](https://github.com/cg123/mergekit) to combine their strengths into a single, superior model.
+
+## Merge Details
+
+This model is a 50/50 SLERP (Spherical Linear Interpolation) merge of two checkpoints from the same training run:
+* **Checkpoint A (Step 60,000):** Excelled at language modeling and reasoning, achieving the best WikiText perplexity and ARC-Easy scores.
+* **Checkpoint B (Step 70,000):** Showed stronger grammatical understanding, achieving the best BLiMP score.
+
+By merging them, this model inherits the best traits of both—achieving lower WikiText perplexity and higher BLiMP/ARC-E scores than either parent checkpoint individually.
+
+## Training Details
+
+* **Parameters:** ~6,000,000
+* **Training Tokens:** 1.14 Billion
+* **Context Length:** 512 tokens
+* **Post-training:** None (This is a base pre-trained model)
+
+### Dataset Mixture
+
+| Dataset | Weight |
+| :--- | :--- |
+| `HuggingFaceFW/fineweb-edu` | 50% |
+| `epfml/FineWeb-HQ` | 30% |
+| `HuggingFaceTB/cosmopedia` (stories split) | 20% |
+
+### Tokenizer
+
+The tokenizer is a basic Byte-Pair Encoding (BPE) tokenizer trained from scratch on a subset of 100,000 samples drawn from the same data mixture. It features a compact vocabulary size of 6,000 plus chatml special tokens for future finetuning.
+
+## Evaluation
+
+Evaluated using the `lm-evaluation-harness` (0-shot).
+
+| Task | Metric | Value |
+| :--- | :--- | :--- |
+| **BLiMP (Avg)** | acc | 0.6523 |
+| ARC-Challenge | acc | 0.1732 |
+| ARC-Challenge | acc_norm | 0.2278 |
+| ARC-Easy | acc | 0.3443 |
+| ARC-Easy | acc_norm | 0.3338 |
+| BoolQ | acc | 0.3801 |
+| HellaSwag | acc | 0.2667 |
+| HellaSwag | acc_norm | 0.2708 |
+| PIQA | acc | 0.5647 |
+| PIQA | acc_norm | 0.5457 |
+| WikiText | bits_per_byte | 1.6987 |
+| WikiText | byte_perplexity | 3.2461 |
+| WikiText | word_perplexity | 542.6415 |
+| Winogrande | acc | 0.5012 |
+
+## Intended Use
+
+As a base model that has not undergone any instruction tuning or alignment, michel-nano is very limited in its raw conversational abilities. It is best suited as a lightweight foundation for fine-tuning on specific downstream tasks. Its small footprint and strong grammatical foundation make it highly adaptable for applications such as:
+* Text classification (e.g., toxic comment detection, sentiment analysis)
+* Lightweight named entity recognition (NER)
+* Random toy projects