Update README.md

README.md

@@ -39,7 +39,7 @@ Hello Orca Mini, what can you do for me?<|eot_id|>
 <|start_header_id|>assistant<|end_header_id|>
 ```
 
-Below shows a code example on how to use this model in default full precision (bf16) format, it requires 
+Below shows a code example on how to use this model in default full precision (bf16) format, it requires around ~130GB VRAM
 
 ```python
 import torch
@@ -59,7 +59,7 @@ outputs = pipeline(messages, max_new_tokens=128, do_sample=True, temperature=0.0
 print(outputs[0]["generated_text"][-1])
 ```
 
-Below shows a code example on how to use this model in 8-bit format via bitsandbytes library
+Below shows a code example on how to use this model in 4-bit format via bitsandbytes library, it requires around ~39GB VRAM
 
 ```python
 import torch
@@ -87,6 +87,31 @@ print(outputs[0]["generated_text"][-1])
 
 ```
 
+Below shows a code example on how to use this model in 8-bit format via bitsandbytes library, it requires around ~69GB VRAM
+
+```python
+import torch
+from transformers import BitsAndBytesConfig, pipeline
+
+model_slug = "pankajmathur/orca_mini_v8_0_70b"
+quantization_config = BitsAndBytesConfig(
+    load_in_8bit=True
+)
+pipeline = pipeline(
+    "text-generation",
+    model=model_slug,
+    model_kwargs={"quantization_config": quantization_config},
+    device_map="auto",
+)
+messages = [
+    {"role": "system", "content": "You are Orca Mini, a helpful AI assistant."},
+    {"role": "user", "content": "Hello Orca Mini, what can you do for me?"}
+]
+outputs = pipeline(messages, max_new_tokens=128, do_sample=True, temperature=0.01, top_k=100, top_p=0.95)
+print(outputs[0]["generated_text"][-1])
+
+```
+
 Below shows a code example on how to do a tool use with this model and tranformer library
 
 Since **orca_mini_v8_0_70b** based upon LLaMA-3.3, it supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).