Update README.md

README.md

@@ -9,14 +9,31 @@ language:
 
 Fine tune of [Smaug 72b v0.1](https://huggingface.co/abacusai/Smaug-72B-v0.1) using an alpaca data set I have handy.  The data is of planning and reasoning, which I use to help allow a model to break down a set of asks into a logical plan.  For some odd reason it bumps the mmlu and winogrande?  I would have expected the ARC to go up over those two, but this is often more of an artform than a science at times.  All thanks to [Albacus.AI](https://huggingface.co/abacusai) for sharing their work.
 
+I used the same dataset in training one of my owl series [Strix Rufipes 70B](https://huggingface.co/ibivibiv/strix-rufipes-70b), which has worked well for planning out development tasks and other technical work.
+
 ![img](./alpaca_dragon.png)
 
 
 
+
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
+```
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("ibivibiv/alpaca-dragon-72b-v1")
+model = AutoModelForCausalLM.from_pretrained("ibivibiv/alpaca-dragon-72b-v1")
+
+inputs = tokenizer("### Instruction: Create a plan for developing the game of snake in python using pygame.\n### Response:\n", return_tensors="pt", return_attention_mask=False)
+
+outputs = model.generate(**inputs, max_length=200)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+
 
 ## Evaluation
 
@@ -82,7 +99,7 @@ Use the code below to get started with the model.
 | hendrycksTest-us_foreign_policy | 94.00        |
 | hendrycksTest-virology          | 57.23        |
 | hendrycksTest-world_religions   | 89.47        |
-| truthfulqa:mc                   | -            |
+| truthfulqa:mc                   | 72.6            |
 | winogrande                      | 86.03        |
 | gsm8k                           | 77.63        |