初始化项目，由ModelHub XC社区提供模型

Model: FlameF0X/TinyMoE-200m-2x16
Source: Original Platform

README.md

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+---
+license: mit
+datasets:
+- HuggingFaceTB/smollm-corpus
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- moe
+- causal-lm
+- mistral
+- tiny-moe
+---
+
+# TinyMoE-200M-2x16
+
+**TinyMoE-200M-2x16** is a compact, highly efficient Sparse Mixture of Experts (MoE) language model built upon the Mixtral/Mistral architecture. Designed for research, edge applications, and resource-constrained environments, this model leverages an expert-routing mechanism to balance a larger total parameter capacity with ultra-low computational overhead during inference.
+
+## Model Details
+
+* **Architecture:** Sparse Mixture of Experts (MoE)
+* **Total Parameters:** ~200m total parameters
+* **Active Parameters per Token:** ~50M active parameters
+* **Expert Configuration:** 16 total local experts, 2 active experts routed per token (`num_experts_per_tok": 2`)
+* **Context Length:** 1024 tokens
+* **Base Architecture:** Mixtral / Mistral For Causal LM
+* **License:** MIT
+
+## Training Data
+
+This model was trained on`FineWeb-Edu-Dedup` 60% and `Cosmopedia-v2` 40%.
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/6615494716917dfdc645c44e/35iLfOIZI-tKMP2TUJ40E.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/6615494716917dfdc645c44e/IyrW-dzolURCavH-AD5a2.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/6615494716917dfdc645c44e/eNsiJy4VXV3zFh2v-qAoF.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/6615494716917dfdc645c44e/37VEpxpu99tJiTAqPJtcT.png)
+![image](https://cdn-uploads.huggingface.co/production/uploads/6615494716917dfdc645c44e/5Z3Xh9xtFnACgO4-qtamg.png)
+
+## Quick Start
+
+You can load and experiment with this model using the Hugging Face `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "FlameF0X/TinyMoE-200m-2x16"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+input_text = "Wikipedia is a free"
+inputs = tokenizer(input_text, return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```