Create README.md

README.md

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+
+---
+pipeline_tag: text-generation
+inference: true
+widget:
+- text: 'Hello!'
+  example_title: Hello world
+  group: Python
+library_name: transformers
+---
+
+This model is randomly initialized, using the config from [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) but with smaller size.
+
+Codes:
+```python
+from optimum.intel.openvino import OVModelForCausalLM
+from transformers import pipeline
+from huggingface_hub import create_repo, upload_folder
+import torch
+import transformers
+import os
+
+model_id = 'mistralai/Mixtral-8x7B-v0.1'
+save_path = '/tmp/yujiepan/mixtral-8xtiny-random'
+repo_id = 'yujiepan/mixtral-8xtiny-random'
+
+config = transformers.AutoConfig.from_pretrained(model_id)
+config.hidden_size = 8
+config.intermediate_size = 32
+config.num_attention_heads = 4
+config.num_experts_per_tok = 2
+config.num_hidden_layers = 2
+config.num_key_value_heads = 2
+config.num_local_experts = 8
+print(config)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
+tokenizer.save_pretrained(save_path)
+
+model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16)
+model = model.half()
+
+pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False, device='cuda')
+print(pipe('Hello World!'))
+
+model.save_pretrained(save_path)
+
+# ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True)
+# ovmodel = ovmodel.half()
+# ovmodel.save_pretrained(save_path)
+
+os.system(f'ls -alh /tmp/yujiepan/mixtral-8xtiny-random')
+create_repo(repo_id, exist_ok=True)
+upload_folder(repo_id=repo_id, folder_path=save_path)
+```
+
+