Update README.md

README.md

@@ -10,12 +10,42 @@ tags:
 
 ![img](./strix_rufipes.png)
 
+# Prompting
+
+## Prompt Template for alpaca style
+
+```
+### Instruction:
+
+<prompt> (without the <>)
+
+### Response:
+```
+
+## Sample Code
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+torch.set_default_device("cuda")
+
+model = AutoModelForCausalLM.from_pretrained("ibivibiv/strix-rufipes-70b", torch_dtype="auto", device_config='auto')
+tokenizer = AutoTokenizer.from_pretrained("ibivibiv/strix-rufipes-70b")
+
+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)
+```
+
 # Model Details
 * **Trained by**: [ibivibiv](https://huggingface.co/ibivibiv)
 * **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
 * **Model type:**  **strix-rufipes-70b** is an auto-regressive language model fine tuned on the Llama 2 transformer architecture.
 * **Language(s)**: English
-* **Purpose**: Has specific training for logic enforcement, will do well in ARC or other logic testing as well as critical thinking tasks.  This model is targeted towards planning exercises.
+* **Purpose**: Has specific training for logic enforcement.  This model is targeted towards planning exercises.
 
 # Benchmark Scores
 
@@ -87,35 +117,7 @@ tags:
 
 
 
-# Prompting
-
-## Prompt Template for alpaca style
-
-```
-### Instruction:
 
-<prompt> (without the <>)
-
-### Response:
-```
-
-## Sample Code
-
-```python
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-torch.set_default_device("cuda")
-
-model = AutoModelForCausalLM.from_pretrained("ibivibiv/strix-rufipes-70b", torch_dtype="auto", device_config='auto')
-tokenizer = AutoTokenizer.from_pretrained("ibivibiv/strix-rufipes-70b")
-
-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)
-```
 
 ## Citations