Create README.md

README.md

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+Trained on 554m tokens, 1 epoch, lr .00987
+brown corpus
+quotes (wikiquote, azquote, gracious quotes, english quotes)
+idioms
+defitions (wordnet)
+wiki_text
+mini pile
+
+code: https://gist.github.com/thistleknot/368ab298edf596ef50d2cfdcbec66fd1
+
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Specify the path to the directory where the model is stored
+#model_dir = r"C:\Users\User\Documents\wiki\wiki\data science\nlp\research\mamba_brown_trained_556m\mamba_brown_trained\mamba_brown_trained"
+model_dir = "/home/user/mamba_brown_trained"
+
+# Load the tokenizer from the local directory
+# Load the tokenizer and model (use a causal language model for text generation)
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForCausalLM.from_pretrained(model_dir)
+model.to('cuda')
+
+# Now, you can use the model and tokenizer for inference
+input_text = "Once upon a time"
+
+# Tokenize the input
+inputs = tokenizer(input_text, return_tensors="pt").to('cuda')
+
+# Generate output tokens using the model
+output_ids = model.generate(**inputs, max_length=50)
+
+# Decode the generated token IDs back into text
+decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+# Print the generated output text
+print(decoded_output)
+```