add download model from openMind_Hub

README.md

@@ -170,12 +170,14 @@ distributed training and inference in a few lines.
 [[blog]](https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution)
 [[HuggingFace model weights]](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base)
 [[Modelscope model weights]](https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-7b-base/summary)
+[[openMind_Hub model weights]](https://modelers.cn/models/HPCAITECH/Colossal-LLaMA-2-7B-base)
 
 - 13B: Construct refined 13B private model with just $5000 USD.
 [[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2)
 [[blog]](https://hpc-ai.com/blog/colossal-llama-2-13b)
 [[HuggingFace model weights]](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-13b-base)
 [[Modelscope model weights]](https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-13b-base/summary)
+[[openMind_Hub model weights]](https://modelers.cn/models/HPCAITECH/Colossal-LLaMA-2-13B-base)
 
 |              Model              |  Backbone  | Tokens Consumed |     MMLU (5-shot)    | CMMLU (5-shot)| AGIEval (5-shot) | GAOKAO (0-shot) | CEval (5-shot)  |
 | :-----------------------------: | :--------: | :-------------: | :------------------: | :-----------: | :--------------: | :-------------: | :-------------: |

applications/Colossal-LLaMA/README.md

@@ -25,6 +25,7 @@ Colossal-LLaMA
   - [Inference](#inference)
     - [Import from HuggingFace](#import-from-huggingface)
     - [Import from Modelscope](#import-from-modelscope)
+    - [Import from openMind_Hub](#import-from-openmind_hub)
     - [Quick Start](#quick-start)
 - [Usage](#usage)
   - [Install](#install)
@@ -259,7 +260,30 @@ inputs = inputs.to('cuda:0')
 output = model.generate(**inputs, **generation_kwargs)
 print(tokenizer.decode(output.cpu()[0], skip_special_tokens=True)[len(input):])
 ```
-You can download model weights from [🤗HuggingFace](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base) or [👾Modelscope](https://modelscope.cn/models/colossalai/Colossal-LLaMA-2-7b-base/summary).
+#### Import from openMind_Hub
+You can also load our model using openMind_Hub, use the following code:
+```Python
+from openmind import AutoModelForCausalLM, AutoTokenizer
+from openmind_hub import snapshot_download
+# Colossal-LLaMA-2-7B-base
+model_dir = snapshot_download('HPCAITECH/Colossal-LLaMA-2-7B-base')
+# Colossal-LLaMA-2-13B-base
+model_dir = snapshot_download('HPCAITECH/Colossal-LLaMA-2-13B-base')
+
+tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True).eval()
+generation_kwargs = {"max_new_tokens": 256,
+                     "top_p": 0.95,
+                     "temperature": 0.3
+                    }
+
+input = '明月松间照，\n\n->\n\n'
+inputs = tokenizer(input, return_token_type_ids=False, return_tensors='pt')
+inputs = inputs.to('cuda:0')
+output = model.generate(**inputs, **generation_kwargs)
+print(tokenizer.decode(output.cpu()[0], skip_special_tokens=True)[len(input):])
+```
+You can download model weights from [🤗HuggingFace](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base) or [👾Modelscope](https://modelscope.cn/models/colossalai/Colossal-LLaMA-2-7b-base/summary) or [openMind_Hub](https://modelers.cn/models/HPCAITECH/Colossal-LLaMA-2-7B-base).
 
 #### Quick Start
 You can run [`inference_example.py`](inference_example.py) to quickly start the inference of our base model by loading model weights from HF.

docs/README-zh-Hans.md

@@ -149,6 +149,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
 [[博客]](https://hpc-ai.com/blog/colossal-llama-2-13b)
 [[HuggingFace 模型权重]](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-13b-base)
 [[Modelscope 模型权重]](https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-13b-base/summary)
+[[openMind_Hub 模型权重]](https://modelers.cn/models/HPCAITECH/Colossal-LLaMA-2-13B-base)
 
 |             Model              |  Backbone  | Tokens Consumed | MMLU (5-shot) | CMMLU (5-shot) | AGIEval (5-shot) | GAOKAO (0-shot) | CEval (5-shot) |
 |:------------------------------:|:----------:|:---------------:|:-------------:|:--------------:|:----------------:|:---------------:|:--------------:|