Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: mit
+license: apache-2.0
 datasets:
 - wikipedia
 language:
@@ -15,13 +15,15 @@ Trained on English and Japanese Wikipedia data.
 
 ## How to use
 
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, set_seed
  
-model = AutoModelForCausalLM.from_pretrained("sbintuiotions/tiny-lm", torch_dtype="auto")
-tokenizer = AutoTokenizer.from_pretrained("sbintuiotions/tiny-lm", use_fast=False)
+model = AutoModelForCausalLM.from_pretrained("sbintuitions/tiny_lm")
+tokenizer = AutoTokenizer.from_pretrained("sbintuitions/tiny_lm", use_fast=False)
 generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-print(generator("Hello", max_length=30, do_sample=True, top_k=100))
+ 
+print(generator("Hello", max_length=30, do_sample=True, top_k=1000))
 ```
 
 ## Model architecture
@@ -31,5 +33,4 @@ A 4-layer, 512-hidden-size transformer-based language model.
 The model was trained on English Wikipedia and Japanese Wikipedia to optimize a traditional language modelling objective for 25B tokens.
 
 ## License
-[MIT License](https://huggingface.co/sbintuitions/tiny-lm/resolve/main/LICENSE)
-
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)