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README.md

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+---
+license: other
+language:
+- en
+pipeline_tag: text-generation
+inference: false
+tags:
+- transformers
+- gguf
+- imatrix
+- Orca-2-13b
+---
+Quantizations of https://huggingface.co/microsoft/Orca-2-13b
+
+
+# From original readme
+
+## Getting started with Orca 2
+
+**Inference with Hugging Face library**
+
+```python
+import torch
+import transformers
+
+if torch.cuda.is_available():
+    torch.set_default_device("cuda")
+else:
+    torch.set_default_device("cpu")
+    
+model = transformers.AutoModelForCausalLM.from_pretrained("microsoft/Orca-2-13b", device_map='auto')
+
+# https://github.com/huggingface/transformers/issues/27132
+# please use the slow tokenizer since fast and slow tokenizer produces different tokens
+tokenizer = transformers.AutoTokenizer.from_pretrained(
+        "microsoft/Orca-2-13b",
+        use_fast=False,
+    )
+
+system_message = "You are Orca, an AI language model created by Microsoft. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior."
+user_message = "How can you determine if a restaurant is popular among locals or mainly attracts tourists, and why might this information be useful?"
+
+prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant"
+
+inputs = tokenizer(prompt, return_tensors='pt')
+output_ids = model.generate(inputs["input_ids"],)
+answer = tokenizer.batch_decode(output_ids)[0]
+
+print(answer)
+
+# This example continues showing how to add a second turn message by the user to the conversation
+second_turn_user_message = "Give me a list of the key points of your first answer."
+
+# we set add_special_tokens=False because we dont want to automatically add a bos_token between messages
+second_turn_message_in_markup = f"\n<|im_start|>user\n{second_turn_user_message}<|im_end|>\n<|im_start|>assistant"
+second_turn_tokens = tokenizer(second_turn_message_in_markup, return_tensors='pt', add_special_tokens=False)
+second_turn_input = torch.cat([output_ids, second_turn_tokens['input_ids']], dim=1)
+
+output_ids_2 = model.generate(second_turn_input,)
+second_turn_answer = tokenizer.batch_decode(output_ids_2)[0]
+
+print(second_turn_answer)
+```