Instructions to use microsoft/Orca-2-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use microsoft/Orca-2-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="microsoft/Orca-2-7b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("microsoft/Orca-2-7b")
model = AutoModelForCausalLM.from_pretrained("microsoft/Orca-2-7b", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use microsoft/Orca-2-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "microsoft/Orca-2-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Orca-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/microsoft/Orca-2-7b

SGLang

How to use microsoft/Orca-2-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "microsoft/Orca-2-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Orca-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "microsoft/Orca-2-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "microsoft/Orca-2-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use microsoft/Orca-2-7b with Docker Model Runner:
```
docker model run hf.co/microsoft/Orca-2-7b
```

Orca-2-7b / README.md

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Update README.md

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---
pipeline_tag: text-generation
tags:
- orca
- orca2
- microsoft
---
# Orca 2

<!-- Provide a quick summary of what the model is/does. -->

Orca 2 is a helpful assistant that is built for research purposes only and provides a single turn response
in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization.
The model is designed to excel particularly in reasoning.

We open-source Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs.

## What is Orca 2’s intended use(s)?

+ Orca 2 is built for research purposes only.
+ The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models.

## How was Orca 2 evaluated?

+ Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer to Section 6 and Appendix in the paper for details on evaluations.

## Model Details

Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters. 
More details about the model can be found at: LINK to Tech Report

Please refer to LLaMA-2 technical report for details on the model architecture.

## License

Orca 2 is licensed under the [Microsoft Research License]().

Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.

## Bias, Risks, and Limitations

Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the
common limitations of other large language models or limitation caused by its training
process, including:

**Data Biases**: Large language models, trained on extensive data, can inadvertently carry
biases present in the source data. Consequently, the models may generate outputs that could
be potentially biased or unfair.

**Lack of Contextual Understanding**: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting
in potential inaccuracies or nonsensical responses.

**Lack of Transparency**: Due to the complexity and size, large language models can act
as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or
decisions. We recommend reviewing transparency notes from Azure for more information.

**Content Harms**: There are various types of content harms that large language models
can cause. It is important to be aware of them when using these models, and to take
actions to prevent them. It is recommended to leverage various content moderation services
provided by different companies and institutions. On an important note, we hope for better
regulations and standards from government and technology leaders around content harms
for AI technologies in future. We value and acknowledge the important role that research
and open source community can play in this direction.

**Hallucination**: It is important to be aware and cautious not to entirely rely on a given
language model for critical decisions or information that might have deep impact as it is
not obvious how to prevent these models from fabricating content. Moreover, it is not clear
whether small models may be more susceptible to hallucination in ungrounded generation
use cases due to their smaller sizes and hence reduced memorization capacities. This is an
active research topic and we hope there will be more rigorous measurement, understanding
and mitigations around this topic.

**Potential for Misuse**: Without suitable safeguards, there is a risk that these models could
be maliciously used for generating disinformation or harmful content.

**Data Distribution**: Orca 2’s performance is likely to correlate strongly with the distribution
of the tuning data. This correlation might limit its accuracy in areas underrepresented in
the training dataset such as math, coding, and reasoning.

**System messages**: Orca 2 demonstrates variance in performance depending on the system
instructions. Additionally, the stochasticity introduced by the model size may lead to
generation of non-deterministic responses to different system instructions.

**Zero-Shot Settings**: Orca 2 was trained on data that mostly simulate zero-shot settings.
While the model demonstrate very strong performance in zero-shot settings, it does not show
the same gains of using few-shot learning compared to other, specially larger, models.

**Synthetic data**: As Orca 2 is trained on synthetic data, it could inherit both the advantages
and shortcomings of the models and methods used for data generation. We posit that Orca
2 benefits from the safety measures incorporated during training and safety guardrails (e.g.,
content filter) within the Azure OpenAI API. However, detailed studies are required for
better quantification of such risks.

This model is solely designed for research settings, and its testing has only been carried
out in such environments. It should not be used in downstream applications, as additional
analysis is needed to assess potential harm or bias in the proposed application.

## 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-2-7b", 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-2-7b",
        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?"
# We use Chat Markup Language https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/ai-services/openai/includes/chat-markup-language.md#working-with-chat-markup-language-chatml
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)
```


**Safe inference with Azure AI Content Safety**

The usage of [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) on top of model prediction is strongly encouraged 
and can help preventing some of content harms. Azure AI Content Safety is a content moderation platform
that uses AI to moderate content. By having Azure AI Content Safety on the output of Orca 2, 
the model output can be moderated by scanning it for different harm categories including sexual content, violence, hate, and 
self-harm with multiple severity levels and multi-lingual detection. 

```python
import os
import math
import transformers
import torch
from azure.ai.contentsafety import ContentSafetyClient
from azure.core.credentials import AzureKeyCredential
from azure.core.exceptions import HttpResponseError
from azure.ai.contentsafety.models import AnalyzeTextOptions
CONTENT_SAFETY_KEY = os.environ["CONTENT_SAFETY_KEY"]
CONTENT_SAFETY_ENDPOINT = os.environ["CONTENT_SAFETY_ENDPOINT"]
# We use Azure AI Content Safety to filter out any content that reaches "Medium" threshold
# For more information: https://learn.microsoft.com/en-us/azure/ai-services/content-safety/
def should_filter_out(input_text, threshold=4):
    # Create an Content Safety client
    client = ContentSafetyClient(CONTENT_SAFETY_ENDPOINT, AzureKeyCredential(CONTENT_SAFETY_KEY))
    # Construct a request
    request = AnalyzeTextOptions(text=input_text)
    # Analyze text
    try:
        response = client.analyze_text(request)
    except HttpResponseError as e:
        print("Analyze text failed.")
        if e.error:
            print(f"Error code: {e.error.code}")
            print(f"Error message: {e.error.message}")
            raise
        print(e)
        raise
    categories = ["hate_result", "self_harm_result", "sexual_result", "violence_result"]
    max_score = -math.inf
    for category in categories:
        max_score = max(max_score, getattr(response, category).severity)
    return max_score >= threshold
model_path = 'microsoft/Orca-2-7b'
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = transformers.AutoModelForCausalLM.from_pretrained(model_path)
model.to(device)
tokenizer = transformers.AutoTokenizer.from_pretrained(
    model_path,
    model_max_length=4096,
    padding_side="right",
    use_fast=False,
    add_special_tokens=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 = "\" \n :You can't just say, \"\"that's crap\"\" and remove it without gaining a consensus. You already know this, based on your block history. —/ \" \nIs the comment obscene? \nOptions : Yes, No."
# We use Chat Markup Language https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/ai-services/openai/includes/chat-markup-language.md#working-with-chat-markup-language-chatml    
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')
inputs = inputs.to(device)
output_ids = model.generate(inputs["input_ids"], max_length=4096, do_sample=False, temperature=0.0, use_cache=True)
sequence_length = inputs["input_ids"].shape[1]
new_output_ids = output_ids[:, sequence_length:]
answers = tokenizer.batch_decode(new_output_ids, skip_special_tokens=True)
final_output = answers[0] if not should_filter_out(answers[0]) else "[Content Filtered]"
print(final_output)
```

	---
	pipeline_tag: text-generation
	tags:
	- orca
	- orca2
	- microsoft
	---

	# Orca 2

	<!-- Provide a quick summary of what the model is/does. -->

	Orca 2 is a helpful assistant that is built for research purposes only and provides a single turn response
	in tasks such as reasoning over user given data, reading comprehension, math problem solving and text summarization.
	The model is designed to excel particularly in reasoning.

	We open-source Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs.

	## What is Orca 2’s intended use(s)?

	+ Orca 2 is built for research purposes only.
	+ The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models.

	## How was Orca 2 evaluated?

	+ Orca 2 has been evaluated on a large number of tasks ranging from reasoning to grounding and safety. Please refer to Section 6 and Appendix in the paper for details on evaluations.

	## Model Details

	Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was moderated using the Microsoft Azure content filters.
	More details about the model can be found at: LINK to Tech Report

	Please refer to LLaMA-2 technical report for details on the model architecture.

	## License

	Orca 2 is licensed under the [Microsoft Research License]().

	Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.

	## Bias, Risks, and Limitations

	Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the
	common limitations of other large language models or limitation caused by its training
	process, including:

	Data Biases: Large language models, trained on extensive data, can inadvertently carry
	biases present in the source data. Consequently, the models may generate outputs that could
	be potentially biased or unfair.

	Lack of Contextual Understanding: Despite their impressive capabilities in language understanding and generation, these models exhibit limited real-world understanding, resulting
	in potential inaccuracies or nonsensical responses.

	Lack of Transparency: Due to the complexity and size, large language models can act
	as “black boxes”, making it difficult to comprehend the rationale behind specific outputs or
	decisions. We recommend reviewing transparency notes from Azure for more information.

	Content Harms: There are various types of content harms that large language models
	can cause. It is important to be aware of them when using these models, and to take
	actions to prevent them. It is recommended to leverage various content moderation services
	provided by different companies and institutions. On an important note, we hope for better
	regulations and standards from government and technology leaders around content harms
	for AI technologies in future. We value and acknowledge the important role that research
	and open source community can play in this direction.

	Hallucination: It is important to be aware and cautious not to entirely rely on a given
	language model for critical decisions or information that might have deep impact as it is
	not obvious how to prevent these models from fabricating content. Moreover, it is not clear
	whether small models may be more susceptible to hallucination in ungrounded generation
	use cases due to their smaller sizes and hence reduced memorization capacities. This is an
	active research topic and we hope there will be more rigorous measurement, understanding
	and mitigations around this topic.

	Potential for Misuse: Without suitable safeguards, there is a risk that these models could
	be maliciously used for generating disinformation or harmful content.

	Data Distribution: Orca 2’s performance is likely to correlate strongly with the distribution
	of the tuning data. This correlation might limit its accuracy in areas underrepresented in
	the training dataset such as math, coding, and reasoning.

	System messages: Orca 2 demonstrates variance in performance depending on the system
	instructions. Additionally, the stochasticity introduced by the model size may lead to
	generation of non-deterministic responses to different system instructions.

	Zero-Shot Settings: Orca 2 was trained on data that mostly simulate zero-shot settings.
	While the model demonstrate very strong performance in zero-shot settings, it does not show
	the same gains of using few-shot learning compared to other, specially larger, models.

	Synthetic data: As Orca 2 is trained on synthetic data, it could inherit both the advantages
	and shortcomings of the models and methods used for data generation. We posit that Orca
	2 benefits from the safety measures incorporated during training and safety guardrails (e.g.,
	content filter) within the Azure OpenAI API. However, detailed studies are required for
	better quantification of such risks.

	This model is solely designed for research settings, and its testing has only been carried
	out in such environments. It should not be used in downstream applications, as additional
	analysis is needed to assess potential harm or bias in the proposed application.

	## 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-2-7b", 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-2-7b",
	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?"

	# We use Chat Markup Language https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/ai-services/openai/includes/chat-markup-language.md#working-with-chat-markup-language-chatml
	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)
	```


	Safe inference with Azure AI Content Safety

	The usage of [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety/) on top of model prediction is strongly encouraged
	and can help preventing some of content harms. Azure AI Content Safety is a content moderation platform
	that uses AI to moderate content. By having Azure AI Content Safety on the output of Orca 2,
	the model output can be moderated by scanning it for different harm categories including sexual content, violence, hate, and
	self-harm with multiple severity levels and multi-lingual detection.

	```python
	import os
	import math
	import transformers
	import torch

	from azure.ai.contentsafety import ContentSafetyClient
	from azure.core.credentials import AzureKeyCredential
	from azure.core.exceptions import HttpResponseError
	from azure.ai.contentsafety.models import AnalyzeTextOptions

	CONTENT_SAFETY_KEY = os.environ["CONTENT_SAFETY_KEY"]
	CONTENT_SAFETY_ENDPOINT = os.environ["CONTENT_SAFETY_ENDPOINT"]

	# We use Azure AI Content Safety to filter out any content that reaches "Medium" threshold
	# For more information: https://learn.microsoft.com/en-us/azure/ai-services/content-safety/
	def should_filter_out(input_text, threshold=4):
	# Create an Content Safety client
	client = ContentSafetyClient(CONTENT_SAFETY_ENDPOINT, AzureKeyCredential(CONTENT_SAFETY_KEY))

	# Construct a request
	request = AnalyzeTextOptions(text=input_text)

	# Analyze text
	try:
	response = client.analyze_text(request)
	except HttpResponseError as e:
	print("Analyze text failed.")
	if e.error:
	print(f"Error code: {e.error.code}")
	print(f"Error message: {e.error.message}")
	raise
	print(e)
	raise

	categories = ["hate_result", "self_harm_result", "sexual_result", "violence_result"]
	max_score = -math.inf
	for category in categories:
	max_score = max(max_score, getattr(response, category).severity)

	return max_score >= threshold

	model_path = 'microsoft/Orca-2-7b'
	device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
	model = transformers.AutoModelForCausalLM.from_pretrained(model_path)
	model.to(device)

	tokenizer = transformers.AutoTokenizer.from_pretrained(
	model_path,
	model_max_length=4096,
	padding_side="right",
	use_fast=False,
	add_special_tokens=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 = "\" \n :You can't just say, \"\"that's crap\"\" and remove it without gaining a consensus. You already know this, based on your block history. —/ \" \nIs the comment obscene? \nOptions : Yes, No."

	# We use Chat Markup Language https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/ai-services/openai/includes/chat-markup-language.md#working-with-chat-markup-language-chatml
	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')
	inputs = inputs.to(device)

	output_ids = model.generate(inputs["input_ids"], max_length=4096, do_sample=False, temperature=0.0, use_cache=True)
	sequence_length = inputs["input_ids"].shape[1]
	new_output_ids = output_ids[:, sequence_length:]
	answers = tokenizer.batch_decode(new_output_ids, skip_special_tokens=True)
	final_output = answers[0] if not should_filter_out(answers[0]) else "[Content Filtered]"

	print(final_output)
	```