初始化项目，由ModelHub XC社区提供模型

Model: SimpleStories/SimpleStories-35M
Source: Original Platform

README.md

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+---
+license: mit
+datasets:
+- lennart-finke/SimpleStories
+language:
+- en
+tags:
+- small-language-model
+- story-generation
+- text-generation
+- efficient-nlp
+- distilled-models
+---
+
+# SimpleStories Model Family
+The SimpleStories models are a tiny model family created for interpretability research, trained on the [SimpleStories dataset](https://huggingface.co/datasets/lennart-finke/SimpleStories).
+
+## Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, LlamaForCausalLM
+
+
+MODEL_SIZE = "35M"
+model_path = "SimpleStories/SimpleStories-{}".format(MODEL_SIZE)
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = LlamaForCausalLM.from_pretrained(model_path)
+model.to("cuda")
+model.eval()
+
+prompt = "The curious cat looked at the"
+
+inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+input_ids = inputs.input_ids.to("cuda")
+
+eos_token_id = 1
+
+with torch.no_grad():
+    output_ids = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=400,
+        temperature=0.7,
+        do_sample=True,
+        eos_token_id=eos_token_id
+)
+
+output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(f"\nGenerated text:\n{output_text}")
+
+```
+
+## Model Variants
+
+| Model Name | n_params | n_layers | d_model | n_heads | n_ctx | d_vocab |
+|------------|----------|----------|---------|---------|-------|---------|
+| SimpleStories-35M | 35 million | 12 | 512 | 8 | 512 | 4096 |
+| SimpleStories-30M | 30 million | 10 | 512 | 8 | 512 | 4096 |
+| SimpleStories-11M | 11 million | 6 | 384 | 6 | 512 | 4096 |
+| SimpleStories-5M | 5 million | 6 | 256 | 4 | 512 | 4096 |
+| SimpleStories-1.25M | 1.25 million | 4 | 128 | 4 | 512 | 4096 |
+
+## Performance Comparison
+Model-evaluated generation quality metrics:
+<p align="center">
+  <img width="80%" src="figures/simplestories_comparison.png">
+</p>
+
+
+## Tokenizer
+
+We use a custom WordPiece tokenizer with a small vocabulary size of 4096. We conducted morphological analysis and coverage gain analysis on the dataset
+to build a small tokenizer without compromising on the quality of generation.
+
+## Dataset
+
+The SimpleStories dataset is a collection of short stories generated by state-of-the-art language models. It features:
+
+- Story annotation with high-level concepts: theme, topic, style, etc.
+- Higher semantic and syntactic diversity through seeded story generation
+- Generated by 2024 models
+- Several NLP-metrics pre-computed to aid filtering
+- ASCII-only guarantee for the English dataset
+
+Read the dataset paper on [arXiv](https://arxiv.org/abs/2504.09184).
+
+## Training
+The training and evaluation scripts can be accessed at https://github.com/danbraunai/simple_stories_train