uploaded readme

README.md

@@ -0,0 +1,73 @@
+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+phi-3-tiny-random - bnb 8bits
+- Model creator: https://huggingface.co/yujiepan/
+- Original model: https://huggingface.co/yujiepan/phi-3-tiny-random/
+
+
+
+
+Original model description:
+---
+library_name: transformers
+pipeline_tag: text-generation
+inference: true
+widget:
+- text: Hello!
+  example_title: Hello world
+  group: Python
+---
+
+This model is randomly initialized, using the config from [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) but with smaller size. 
+Note the model is in float16.
+
+Codes:
+```python
+import transformers
+import torch
+import os
+from huggingface_hub import create_repo, upload_folder
+
+source_model_id = 'microsoft/Phi-3-mini-128k-instruct'
+save_path = '/tmp/yujiepan/phi-3-tiny-random'
+repo_id = 'yujiepan/phi-3-tiny-random'
+
+config = transformers.AutoConfig.from_pretrained(
+    source_model_id, trust_remote_code=True)
+config.hidden_size = 16
+config.intermediate_size = 32
+config.num_attention_heads = 4
+config.num_hidden_layers = 2
+config.num_key_value_heads = 4
+config.rope_scaling['long_factor'] = [1.0299, 1.0499]
+config.rope_scaling['short_factor'] = [1.05, 1.05]
+
+model = transformers.AutoModelForCausalLM.from_config(
+    config, trust_remote_code=True)
+model = model.to(torch.float16)
+model.save_pretrained(save_path)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(
+    source_model_id, trust_remote_code=True)
+tokenizer.save_pretrained(save_path)
+
+result = transformers.pipelines.pipeline(
+    'text-generation',
+    model=model.float(), tokenizer=tokenizer)('Hello')
+print(result)
+
+os.system(f'ls -alh {save_path}')
+create_repo(repo_id, exist_ok=True)
+upload_folder(repo_id=repo_id, folder_path=save_path)
+
+from transformers import AutoProcessor
+AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True).push_to_hub(repo_id)
+```
+