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  <div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div>
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  <p align="center">
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- 🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a> • 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 👾 <a href="https://wisemodel.cn/organization/Skywork" target="_blank">Wisemodel</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="http://arxiv.org/abs/2310.19341" target="_blank">Tech Report</a>
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  </p>
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  <div align="center">
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  | | HuggingFace Model | ModelScope Model | Wisemodel Model |
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  |:-------:|:-----------:|:-----------------------------:|:-----------------------------:|
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- | **Skywork-MoE-base** | 🤗 [Skywork-MoE-base](https://huggingface.co/Skywork/Skywork-MoE-base) | 🤖[Skywork-MoE-base](https://www.modelscope.cn/models/skywork/Skywork-MoE-base) | 👾[Skywork-MoE-base](https://wisemodel.cn/models/Skywork/Skywork-MoE-base) |
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- | **Skywork-MoE-Base-FP8** | 🤗 [Skywork-MoE-Base-FP8](https://huggingface.co/Skywork/Skywork-MoE-Base-FP8) | 🤖 | 👾 |
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  # Benchmark Results
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  We evaluated Skywork-MoE-base model on various popular benchmarks, including C-Eval, MMLU, CMMLU, GSM8K, MATH and HumanEval.
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  <img src="misc/skywork_moe_base_evaluation.png" alt="Image" width="600" height="280">
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- # Demonstration of Hugging Face Model Inference
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-
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- ## Base Model Inference
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-
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- We can perform inference for the Skywork-MoE-base (16x13B size) model using HuggingFace on 8xA100/A800 or higher GPU hardware configurations.
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-
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- ```python
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-
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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-
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- model = AutoModelForCausalLM.from_pretrained("Skywork/Skywork-MoE-base", trust_remote_code=True, device_map='auto')
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- tokenizer = AutoTokenizer.from_pretrained("Skywork/Skywork-MoE-base", trust_remote_code=True)
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-
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- inputs = tokenizer('陕西的省会是西安', return_tensors='pt').to(model.device)
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- response = model.generate(inputs.input_ids, max_length=128)
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- print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True))
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- """
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- 陕西的省会是西安。
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- 西安,古称长安、镐京,是陕西省会、副省级市、关中平原城市群核心城市、丝绸之路起点城市、“一带一路”核心区、中国西部地区重要的中心城市,国家重要的科研、教育、工业基地。
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- 西安是中国四大古都之一,联合国科教文组织于1981年确定的“世界历史名城”,美媒评选的世界十大古都之一。地处关中平原中部,北濒渭河,南依秦岭,八水润长安。下辖11区2县并代管西
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- """
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-
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- inputs = tokenizer('陕西的省会是西安,甘肃的省会是兰州,河南的省会是郑州', return_tensors='pt').to(model.device)
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- response = model.generate(inputs.input_ids, max_length=128)
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- print(tokenizer.decode(response.cpu()[0], skip_special_tokens=True))
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- """
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- 陕西的省会是西安,甘肃的省会是兰州,河南的省会是郑州,湖北的省会是武汉,湖南的省会是长沙,安徽的省会是合肥,江西的省会是南昌,江苏的省会是南京,浙江的省会是杭州,福建的省会是福州,广东的省会是广州,广西的省会是南宁,四川的省会是成都,贵州的省会是贵阳,云南的省会是昆明,山西的省会是太原,山东的省会是济南,河北的省会是石家庄,辽宁的省会是沈阳,吉林的省会是长春,黑龙江的
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- """
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-
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- ```
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-
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  # Demonstration of vLLM Model Inference
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  <div align="center"><img src="misc/skywork_logo.jpeg" width="550"/></div>
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  <p align="center">
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+ 🤗 <a href="https://huggingface.co/Skywork" target="_blank">Hugging Face</a> • 🤖 <a href="https://modelscope.cn/organization/Skywork" target="_blank">ModelScope</a> • 👾 <a href="https://wisemodel.cn/organization/Skywork" target="_blank">Wisemodel</a> • 💬 <a href="https://github.com/SkyworkAI/Skywork/blob/main/misc/wechat.png?raw=true" target="_blank">WeChat</a>• 📜<a href="https://github.com/SkyworkAI/Skywork-MoE/blob/main/skywork-moe-tech-report.pdf" target="_blank">Tech Report</a>
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  </p>
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  <div align="center">
 
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  | | HuggingFace Model | ModelScope Model | Wisemodel Model |
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  |:-------:|:-----------:|:-----------------------------:|:-----------------------------:|
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+ | **Skywork-MoE-base** | 🤗 [Skywork-MoE-base](https://github.com/SkyworkAI/Skywork-MoE) | 🤖[Skywork-MoE-base](https://www.modelscope.cn/models/skywork/Skywork-MoE-base) | 👾[Skywork-MoE-base](https://wisemodel.cn/models/Skywork/Skywork-MoE-base) |
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+ | **Skywork-MoE-Base-FP8** | 🤗 [Skywork-MoE-Base-FP8](https://github.com/SkyworkAI/Skywork-MoE) | 🤖 | 👾 |
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  # Benchmark Results
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  We evaluated Skywork-MoE-base model on various popular benchmarks, including C-Eval, MMLU, CMMLU, GSM8K, MATH and HumanEval.
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  <img src="misc/skywork_moe_base_evaluation.png" alt="Image" width="600" height="280">
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  # Demonstration of vLLM Model Inference
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