Llama-3.1-8B-Instruct Overview

Description:

Llama-3.1-8B-Instruct is an 8 billion parameter, instruction-tuned large language model created by Meta. This model is part of the Llama 3.1 family of open-access models and is specifically optimized for dialogue and conversational use cases, making it highly capable of following user instructions to perform a wide variety of natural language processing tasks.

This model is ready for commercial/non-commercial use.

Third-Party Community Consideration

This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [meta-llama/Llama-3.1-8B-Instruct]
\n(https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).

License/Terms of Use:

GOVERNING TERMS: NVIDIA Open Model License.

ADDITIONAL INFORMATION: Llama 3.1 Community License Agreement. Built with Llama.

Get Help

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

Deployment Geography:

Global

Use Case:

This model is primarily used by developers, researchers, and businesses to build and experiment with a wide range of generative AI applications. Its combination of strong performance, efficiency, and an open-access license makes it highly versatile.

Developers and Businesses would use this model to create production-ready applications such as:\n
- AI Chatbots and customer service agents.
- Content creation tools for writing emails, marketing copy, and articles.
- Summarization and question-answering systems for internal documents.
- Code generation assistants to help programmers write and debug code.
\n
Researchers would use it to study large language model behavior, explore AI safety and alignment, and benchmark new training or fine-tuning techniques on a capable, open model.
AI Hobbyists would use the model for personal projects, running it on consumer-grade hardware to experiment with creating their own AI assistants or exploring the frontiers of generative AI.

Release Date:

Build.NVIDIA.com 07/23/2024 via
\nhttps://build.nvidia.com/meta/llama-3_1-8b-instruct

Huggingface 07/23/2024 via
\nhttps://huggingface.co/meta-llama/Llama-3.1-8B-Instruct

Reference(s):

https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Llama-3.1-8B

This model was developed based on Meta Llama-3.1-8B
\nhttps://huggingface.co/meta-llama/Llama-3.1-8B-Instruct

Number of model parameters: 8.03*10^9

Input:

Input Type(s): Text

Input Format(s): String

Input Parameters: One-Dimensional (1D)

Other Properties Related to Input: The primary input limit is the model's maximum context length, which is 128,000 tokens. All input text must be pre-processed using the model's specific tokenizer to convert the string into a sequence of token IDs. For conversational use, inputs should be formatted using the model's designated chat template.

Output:

Output Type(s): Text

Output Format(s): String

Output Parameters: One-Dimensional (1D)

Other Properties Related to Output: The output consists of a variable-length sequence of tokens from the model's vocabulary. The total length of the input and output cannot exceed the 128,000-token context window. Post-processing is required to detokenize the raw token ID sequence into a human-readable string.

Software Integration:

Runtime Engine: vLLM, TensorRT

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
\nNVIDIA Blackwell
\nNVIDIA Hopper
\nNVIDIA Lovelace

Preferred Operating System(s):

Linux
\nWindows
\nmacOS

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

Llama-3.1-8B-Instruct

Usage

Use with transformers

Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.

Make sure to update your transformers installation via pip install --upgrade transformers.

import transformers\nimport torch\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n\npipeline = transformers.pipeline(\n    \"text-generation\",\n    model=model_id,\n    model_kwargs={\"torch_dtype\": torch.bfloat16},\n    device_map=\"auto\",\n)\n\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are a pirate chatbot who always responds in pirate speak!\"},\n    {\"role\": \"user\", \"content\": \"Who are you?\"},\n]\n\noutputs = pipeline(\n    messages,\n    max_new_tokens=256,\n)\nprint(outputs[0][\"generated_text\"][-1])\n

Note: You can also find detailed recipes on how to use the model locally, with torch.compile(), assisted generations, quantised and more at huggingface-llama-recipes

Tool use with transformers

LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting here.

Tool use is also supported through chat templates in Transformers. Here is a quick example showing a single simple tool:

float:\n """\n Get the current temperature at a location.\n \n Args:\n location: The location to get the temperature for, in the format "City, Country"\n Returns:\n The current temperature at the specified location in the specified units, as a float.\n """\n return 22. # A real function should probably actually get the temperature!\n\n# Next, create a chat and apply the chat template\nmessages = [\n {"role": "system", "content": "You are a bot that responds to weather queries."},\n {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}\n]\n\ninputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)\n\">

# First, define a tool\ndef get_current_temperature(location: str) -> float:\n    \"\"\"\n    Get the current temperature at a location.\n    \n    Args:\n        location: The location to get the temperature for, in the format \"City, Country\"\n    Returns:\n        The current temperature at the specified location in the specified units, as a float.\n    \"\"\"\n    return 22.  # A real function should probably actually get the temperature!\n\n# Next, create a chat and apply the chat template\nmessages = [\n  {\"role\": \"system\", \"content\": \"You are a bot that responds to weather queries.\"},\n  {\"role\": \"user\", \"content\": \"Hey, what's the temperature in Paris right now?\"}\n]\n\ninputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)\n

You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:

tool_call = {\"name\": \"get_current_temperature\", \"arguments\": {\"location\": \"Paris, France\"}}\nmessages.append({\"role\": \"assistant\", \"tool_calls\": [{\"type\": \"function\", \"function\": tool_call}]})\n

and then call the tool and append the result, with the tool role, like so:

messages.append({\"role\": \"tool\", \"name\": \"get_current_temperature\", \"content\": \"22.0\"})\n

After that, you can generate() again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information, see the LLaMA prompt format docs and the Transformers tool use documentation.

Training, Testing, and Evaluation Datasets:

Training Dataset

Data Modality: Text

Link: Undisclosed

Data Collection Method: Hybrid: Human, Synthetic, Automated

Labeling Method: Hybrid: Human, Automated

Properties:

The model was pre-trained on a dataset of over 15 trillion tokens. This dataset is a high-quality mix of publicly available data, heavily filtered for safety and quality. The instruction fine-tuning dataset is smaller and consists of high-quality prompts, responses, and preference rankings curated by humans.

Testing Dataset

Link: Undisclosed

Data Collection Method: Hybrid: Human, Automated

Labeling Method: Human

Properties:

The testing datasets comprise thousands of individual problems designed to measure model capabilities in specific areas:

General Knowledge & Reasoning: MMLU, DROP, AGIEval, BIG-Bench Hard
Mathematics: GSM8K, MATH
Coding: HumanEval

Evaluation Dataset

Link: Undisclosed

Data Collection Method: Hybrid: Human, Automated

Labeling Method: Human

Properties:

The evaluation datasets consist of thousands of questions and problems designed to test the model's capabilities in areas like general knowledge, reasoning, mathematics, and coding.

Base pretrained models

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Category	Benchmark	# Shots	Metric	Llama 3 8B	Llama 3.1 8B	Llama 3 70B	Llama 3.1 70B	Llama 3.1 405B
General	MMLU	5	macro_avg/acc_char	66.7	66.7	79.5	79.3	85.2
	MMLU-Pro (CoT)	5	macro_avg/acc_char	36.2	37.1	55.0	53.8	61.6
	AGIEval English	3-5	average/acc_char	47.1	47.8	63.0	64.6	71.6
	CommonSenseQA	7	acc_char	72.6	75.0	83.8	84.1	85.8
	Winogrande	5	acc_char	-	60.5	-	83.3	86.7
	BIG-Bench Hard (CoT)	3	average/em	61.1	64.2	81.3	81.6	85.9
	ARC-Challenge	25	acc_char	79.4	79.7	93.1	92.9	96.1
Knowledge reasoning	TriviaQA-Wiki	5	em	78.5	77.6	89.7	89.8	91.8
Reading comprehension	SQuAD	1	em	76.4	77.0	85.6	81.8	89.3
	QuAC (F1)	1	f1	44.4	44.9	51.1	51.1	53.6
	BoolQ	0	acc_char	75.7	75.0	79.0	79.4	80.0
	DROP (F1)	3	f1	58.4	59.5	79.7	79.6	84.8

Instruction tuned models

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Category	Benchmark	# Shots	Metric	Llama 3 8B Instruct	Llama 3.1 8B Instruct	Llama 3 70B Instruct	Llama 3.1 70B Instruct	Llama 3.1 405B Instruct
General	MMLU	5	macro_avg/acc	68.5	69.4	82.0	83.6	87.3
	MMLU (CoT)	0	macro_avg/acc	65.3	73.0	80.9	86.0	88.6
	MMLU-Pro (CoT)	5	micro_avg/acc_char	45.5	48.3	63.4	66.4	73.3
	IFEval			76.8	80.4	82.9	87.5	88.6
Reasoning	ARC-C	0	acc	82.4	83.4	94.4	94.8	96.9
	GPQA	0	em	34.6	30.4	39.5	46.7	50.7
Code	HumanEval	0	pass@1	60.4	72.6	81.7	80.5	89.0
	MBPP ++ base version	0	pass@1	70.6	72.8	82.5	86.0	88.6
	Multipl-E HumanEval	0	pass@1	-	50.8	-	65.5	75.2
	Multipl-E MBPP	0	pass@1	-	52.4	-	62.0	65.7
Math	GSM-8K (CoT)	8	em_maj1@1	80.6	84.5	93.0	95.1	96.8
	MATH (CoT)	0	final_em	29.1	51.9	51.0	68.0	73.8
Tool Use	API-Bank	0	acc	48.3	82.6	85.1	90.0	92.0
	BFCL	0	acc	60.3	76.1	83.0	84.8	88.5
	Gorilla Benchmark API Bench	0	acc	1.7	8.2	14.7	29.7	35.3
	Nexus (0-shot)	0	macro_avg/acc	18.1	38.5	47.8	56.7	58.7
Multilingual	Multilingual MGSM (CoT)	0	em	-	68.9	-	86.9	91.6

Multilingual benchmarks

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Category	Benchmark	Language	Llama 3.1 8B	Llama 3.1 70B	Llama 3.1 405B
General	MMLU (5-shot, macro_avg/acc)	Portuguese	62.12	80.13	84.95
		Spanish	62.45	80.05	85.08
		Italian	61.63	80.4	85.04
		German	60.59	79.27	84.36
		French	62.34	79.82	84.66
		Hindi	50.88	74.52	80.31
		Thai	50.32	72.95	78.21

Technical Limitations

Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, the model's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying this model in any applications, developers should perform safety testing and tuning tailored to their specific applications. Please refer to available resources including the Responsible Use Guide, Trust and Safety solutions, and other resources to learn more about responsible development.

Inference:

Acceleration Engine: vLLM, TensorRT

Test Hardware:

DC P0:

B200 SXM
H200 SXM
H100 SXM
A100 SXM 40GB
A100 SXM 80GB
A10G
L40S PCIe
H100 NVL
H200 NVL
GB200 NVL72
GH200 NVL2
GH200 96GB
RTX 6000 Pro BSE
DGX Spark

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns here.

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

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This model is part of the Llama 3.1 family of open-access models and is specifically optimized for dialogue and conversational use cases, making it highly capable of following user instructions to perform a wide variety of natural language processing tasks.\n\nThis model is ready for commercial/non-commercial use.\n\n## **Third-Party Community Consideration**\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA \\[meta-llama/Llama-3.1-8B-Instruct\\] \n([https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)). \n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). \n\n**ADDITIONAL INFORMATION**: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.\n\n## **Get Help**\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.\n\n## **Deployment Geography:**\n\nGlobal \n\n## **Use Case:**\n\nThis model is primarily used by developers, researchers, and businesses to build and experiment with a wide range of generative AI applications. Its combination of strong performance, efficiency, and an open-access license makes it highly versatile.\n\n* Developers and Businesses would use this model to create production-ready applications such as: \n * AI Chatbots and customer service agents. \n * Content creation tools for writing emails, marketing copy, and articles. \n * Summarization and question-answering systems for internal documents. \n * Code generation assistants to help programmers write and debug code. \n* Researchers would use it to study large language model behavior, explore AI safety and alignment, and benchmark new training or fine-tuning techniques on a capable, open model. \n* AI Hobbyists would use the model for personal projects, running it on consumer-grade hardware to experiment with creating their own AI assistants or exploring the frontiers of generative AI.\n\n## **Release Date:**\n\nBuild.NVIDIA.com 07/23/2024 via \n[https://build.nvidia.com/meta/llama-3_1-8b-instruct](https://build.nvidia.com/meta/llama-3_1-8b-instruct)\n\nHuggingface 07/23/2024 via \n[https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)\n\n\n## **Reference(s):** \n\n[https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) \n\n## **Model Architecture:** \n\nArchitecture Type: Transformer \nNetwork Architecture: Llama-3.1-8B\n\nThis model was developed based on Meta Llama-3.1-8B \n[https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) \n\nNumber of model parameters: 8.03*10^9\n\n## **Input:**\n\nInput Type(s): Text \n\nInput Format(s): String \n\nInput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Input: The primary input limit is the model's maximum context length, which is 128,000 tokens. All input text must be pre-processed using the model's specific tokenizer to convert the string into a sequence of token IDs. For conversational use, inputs should be formatted using the model's designated chat template.\n\n## **Output:**\n\nOutput Type(s): Text \n\nOutput Format(s): String\n\nOutput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Output: The output consists of a variable-length sequence of tokens from the model's vocabulary. The total length of the input and output cannot exceed the 128,000-token context window. Post-processing is required to detokenize the raw token ID sequence into a human-readable string.\n\n## **Software Integration:**\n\nRuntime Engine: vLLM, TensorRT\n\nSupported Hardware Microarchitecture Compatibility:\n\nNVIDIA Ampere \nNVIDIA Blackwell \nNVIDIA Hopper \nNVIDIA Lovelace \n\nPreferred Operating System(s):\n\nLinux \nWindows \nmacOS\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## **Model Version(s):**\n\nLlama-3.1-8B-Instruct
\n\n## **Usage**\n\n### **Use with transformers**\n\nStarting with transformers \\>= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.\n\nMake sure to update your transformers installation via pip install \\--upgrade transformers.\n\n```\nimport transformers\nimport torch\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n\npipeline = transformers.pipeline(\n \"text-generation\",\n model=model_id,\n model_kwargs={\"torch_dtype\": torch.bfloat16},\n device_map=\"auto\",\n)\n\nmessages = [\n {\"role\": \"system\", \"content\": \"You are a pirate chatbot who always responds in pirate speak!\"},\n {\"role\": \"user\", \"content\": \"Who are you?\"},\n]\n\noutputs = pipeline(\n messages,\n max_new_tokens=256,\n)\nprint(outputs[0][\"generated_text\"][-1])\n```\n\nNote: You can also find detailed recipes on how to use the model locally, with torch.compile(), assisted generations, quantised and more at [huggingface-llama-recipes](https://github.com/huggingface/huggingface-llama-recipes)\n\n### **Tool use with transformers**\n\nLLaMA-3.1 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/).\n\nTool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. Here is a quick example showing a single simple tool:\n\n```\n# First, define a tool\ndef get_current_temperature(location: str) -> float:\n \"\"\"\n Get the current temperature at a location.\n \n Args:\n location: The location to get the temperature for, in the format \"City, Country\"\n Returns:\n The current temperature at the specified location in the specified units, as a float.\n \"\"\"\n return 22. # A real function should probably actually get the temperature!\n\n# Next, create a chat and apply the chat template\nmessages = [\n {\"role\": \"system\", \"content\": \"You are a bot that responds to weather queries.\"},\n {\"role\": \"user\", \"content\": \"Hey, what's the temperature in Paris right now?\"}\n]\n\ninputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)\n```\n\nYou can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:\n\n```\ntool_call = {\"name\": \"get_current_temperature\", \"arguments\": {\"location\": \"Paris, France\"}}\nmessages.append({\"role\": \"assistant\", \"tool_calls\": [{\"type\": \"function\", \"function\": tool_call}]})\n```\n\nand then call the tool and append the result, with the tool role, like so:\n\n```\nmessages.append({\"role\": \"tool\", \"name\": \"get_current_temperature\", \"content\": \"22.0\"})\n```\n\nAfter that, you can generate() again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling \\- for more information, see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).\n\n## **Training, Testing, and Evaluation Datasets:**\n\n### **Training Dataset**\n\n**Data Modality:** Text \n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method:** Hybrid: Human, Automated\n\n**Properties:** \n\nThe model was pre-trained on a dataset of over 15 trillion tokens. This dataset is a high-quality mix of publicly available data, heavily filtered for safety and quality. The instruction fine-tuning dataset is smaller and consists of high-quality prompts, responses, and preference rankings curated by humans.\n\n### **Testing Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Automated\n\n**Labeling Method:** Human\n\n**Properties:** \n\nThe testing datasets comprise thousands of individual problems designed to measure model capabilities in specific areas:\n\n* **General Knowledge & Reasoning:** MMLU, DROP, AGIEval, BIG-Bench Hard \n* **Mathematics:** GSM8K, MATH \n* **Coding:** HumanEval\n\n### **Evaluation Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Automated\n\n**Labeling Method:** Human\n\n**Properties:** \n\nThe evaluation datasets consist of thousands of questions and problems designed to test the model's capabilities in areas like general knowledge, reasoning, mathematics, and coding.\n\n**Base pretrained models**\n\n| Category | Benchmark | \\# Shots | Metric | Llama 3 8B | Llama 3.1 8B | Llama 3 70B | Llama 3.1 70B | Llama 3.1 405B |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU | 5 | macro\\_avg/acc\\_char | 66.7 | 66.7 | 79.5 | 79.3 | 85.2 |\n| | MMLU-Pro (CoT) | 5 | macro\\_avg/acc\\_char | 36.2 | 37.1 | 55.0 | 53.8 | 61.6 |\n| | AGIEval English | 3-5 | average/acc\\_char | 47.1 | 47.8 | 63.0 | 64.6 | 71.6 |\n| | CommonSenseQA | 7 | acc\\_char | 72.6 | 75.0 | 83.8 | 84.1 | 85.8 |\n| | Winogrande | 5 | acc\\_char | \\- | 60.5 | \\- | 83.3 | 86.7 |\n| | BIG-Bench Hard (CoT) | 3 | average/em | 61.1 | 64.2 | 81.3 | 81.6 | 85.9 |\n| | ARC-Challenge | 25 | acc\\_char | 79.4 | 79.7 | 93.1 | 92.9 | 96.1 |\n| Knowledge reasoning | TriviaQA-Wiki | 5 | em | 78.5 | 77.6 | 89.7 | 89.8 | 91.8 |\n| Reading comprehension | SQuAD | 1 | em | 76.4 | 77.0 | 85.6 | 81.8 | 89.3 |\n| | QuAC (F1) | 1 | f1 | 44.4 | 44.9 | 51.1 | 51.1 | 53.6 |\n| | BoolQ | 0 | acc\\_char | 75.7 | 75.0 | 79.0 | 79.4 | 80.0 |\n| | DROP (F1) | 3 | f1 | 58.4 | 59.5 | 79.7 | 79.6 | 84.8 |\n\n**Instruction tuned models**\n\n| Category | Benchmark | \\# Shots | Metric | Llama 3 8B Instruct | Llama 3.1 8B Instruct | Llama 3 70B Instruct | Llama 3.1 70B Instruct | Llama 3.1 405B Instruct |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU | 5 | macro\\_avg/acc | 68.5 | 69.4 | 82.0 | 83.6 | 87.3 |\n| | MMLU (CoT) | 0 | macro\\_avg/acc | 65.3 | 73.0 | 80.9 | 86.0 | 88.6 |\n| | MMLU-Pro (CoT) | 5 | micro\\_avg/acc\\_char | 45.5 | 48.3 | 63.4 | 66.4 | 73.3 |\n| | IFEval | | | 76.8 | 80.4 | 82.9 | 87.5 | 88.6 |\n| Reasoning | ARC-C | 0 | acc | 82.4 | 83.4 | 94.4 | 94.8 | 96.9 |\n| | GPQA | 0 | em | 34.6 | 30.4 | 39.5 | 46.7 | 50.7 |\n| Code | HumanEval | 0 | pass@1 | 60.4 | 72.6 | 81.7 | 80.5 | 89.0 |\n| | MBPP \\++ base version | 0 | pass@1 | 70.6 | 72.8 | 82.5 | 86.0 | 88.6 |\n| | Multipl-E HumanEval | 0 | pass@1 | \\- | 50.8 | \\- | 65.5 | 75.2 |\n| | Multipl-E MBPP | 0 | pass@1 | \\- | 52.4 | \\- | 62.0 | 65.7 |\n| Math | GSM-8K (CoT) | 8 | em\\_maj1@1 | 80.6 | 84.5 | 93.0 | 95.1 | 96.8 |\n| | MATH (CoT) | 0 | final\\_em | 29.1 | 51.9 | 51.0 | 68.0 | 73.8 |\n| Tool Use | API-Bank | 0 | acc | 48.3 | 82.6 | 85.1 | 90.0 | 92.0 |\n| | BFCL | 0 | acc | 60.3 | 76.1 | 83.0 | 84.8 | 88.5 |\n| | Gorilla Benchmark API Bench | 0 | acc | 1.7 | 8.2 | 14.7 | 29.7 | 35.3 |\n| | Nexus (0-shot) | 0 | macro\\_avg/acc | 18.1 | 38.5 | 47.8 | 56.7 | 58.7 |\n| Multilingual | Multilingual MGSM (CoT) | 0 | em | \\- | 68.9 | \\- | 86.9 | 91.6 |\n\n**Multilingual benchmarks**\n\n| Category | Benchmark | Language | Llama 3.1 8B | Llama 3.1 70B | Llama 3.1 405B |\n| :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU (5-shot, macro\\_avg/acc) | Portuguese | 62.12 | 80.13 | 84.95 |\n| | | Spanish | 62.45 | 80.05 | 85.08 |\n| | | Italian | 61.63 | 80.4 | 85.04 |\n| | | German | 60.59 | 79.27 | 84.36 |\n| | | French | 62.34 | 79.82 | 84.66 |\n| | | Hindi | 50.88 | 74.52 | 80.31 |\n| | | Thai | 50.32 | 72.95 | 78.21 |\n\n## **Technical Limitations** \n\n Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, the model's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying this model in any applications, developers should perform safety testing and tuning tailored to their specific applications. Please refer to available resources including the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. \n\n## **Inference:**\n\n**Acceleration Engine:** vLLM, TensorRT \n\n**Test Hardware:** \n\nDC P0:
\n* B200 SXM
\n* H200 SXM
\n* H100 SXM
\n* A100 SXM 40GB
\n* A100 SXM 80GB
\n* A10G
\n* L40S PCIe
\n* H100 NVL
\n* H200 NVL
\n* GB200 NVL72
\n* GH200 NVL2
\n* GH200 96GB
\n* RTX 6000 Pro BSE
\n* DGX Spark
\n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.","displayName","Llama-3.1-8b-instruct",["D",1768337583610],"publisher","Meta","latestVersion","collectionCounts","imageUrl","/api/image/nim/meta/models/llama-3.1-8b-instruct","rawImageUrl","https://assets.ngc.nvidia.com/products/api-catalog/images/llama-3_1-8b-instruct.jpg","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[604,605],"displayLabels",[585,586,587,588,589],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_554":602,"_591":602,"_592":603,"_594":595},{"_554":550,"_591":550,"_592":601,"_594":595},{"_554":599,"_591":599,"_592":600,"_594":595},{"_554":597,"_591":597,"_592":598,"_594":595},{"_554":590,"_591":590,"_592":593,"_594":595},"NSPECT-0DQP-LNLV","value","filter",{"_596":595,"_591":590},"category","label","field","NIM",{"_596":595,"_591":597},"Llama3.1",{"_596":595,"_591":599},{"_596":595,"_591":550},"False",{"_596":595,"_591":602},{"_554":611,"_591":611,"_592":612,"_594":609},{"_554":606,"_591":607,"_592":608,"_594":609},"NVIDIA NIM","nimmcro_nvidia_nim",{"_596":610,"_591":607},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_596":595,"_591":611},{"_176":1596,"_178":1597},"ngcApps",[1549],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1530":1531,"_1532":1533,"_1534":1535,"_1536":1537,"_1538":624,"_207":652,"_1539":1540,"_1541":60,"_1542":100,"_1543":100,"_1544":1545,"_1546":100,"_1547":1548},"amplitude",{"_1527":1528,"_1164":-7,"_1529":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1521":1522,"_1523":1524,"_1525":1526},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1785880352179,"1.11.0","defaultSearchSuggestions",{"_1404":237,"_1405":1406,"_1407":1408,"_1409":1410},"announcements",[],"consentState",{"_690":1394,"_1395":35,"_1396":35,"_1397":100,"_1398":1399},"consentConfig",{"_672":673,"_674":675,"_676":677,"_678":679,"_680":681,"_682":683,"_684":685,"_686":687,"_688":689,"_690":691},"showContactAdmin",1785880352180,[],[],[666,667,668,669,670,671],{"_49":64,"_51":75,"_53":171,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":71,"_53":171,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":69,"_53":171,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":65,"_53":171,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":58,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[782],{"_89":783,"_737":780,"_738":35,"_236":784},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. 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YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","demdex.net",[803],{"_89":804,"_737":801,"_738":35,"_236":805},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[808],{"_89":809,"_737":806,"_738":35,"_236":810},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[813],{"_89":814,"_737":811,"_738":35,"_236":774},"R","6sc.co",[817],{"_89":818,"_737":815,"_738":35,"_236":819},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","AMP_*","build.nvidia.com","Supports Amplitude product analytics.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945],[892,893,894,895,896],{"_731":806,"_554":806,"_236":237,"_704":916},{"_731":780,"_554":780,"_236":237,"_704":911},{"_731":906,"_554":906,"_236":237,"_704":907},{"_731":901,"_554":901,"_236":237,"_704":902},{"_731":897,"_554":897,"_236":237,"_704":898},"nvidia.partners",[899],{"_89":900,"_737":897,"_738":35,"_236":774},"edge","app.hushly.com",[903],{"_89":904,"_737":901,"_738":35,"_236":905},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[908],{"_89":909,"_737":906,"_738":35,"_236":910},"_octo","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[1004,1005,1006,1007,1008,1009,1010,1011,1012,1013,1014,1015,1016,1017,1018,1019,1020,1021,1022,1023,1024,1025,1026,1027,1028,1029,1030,1031,1032],[984,985,986,987],{"_731":999,"_554":999,"_236":237,"_704":1000},{"_731":994,"_554":994,"_236":237,"_704":995},{"_731":793,"_554":793,"_236":237,"_704":991},{"_731":906,"_554":906,"_236":237,"_704":988},[989],{"_89":990,"_737":906,"_738":100,"_236":910},"_gh_sess",[992],{"_89":993,"_737":793,"_738":100,"_236":800},"YSC","bizible.com",[996],{"_89":997,"_737":994,"_738":35,"_236":998},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[1001],{"_89":1002,"_737":999,"_738":35,"_236":1003},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_89":1074,"_737":852,"_738":35,"_236":1075},{"_89":1059,"_737":1073,"_738":100,"_236":1060},{"_89":1042,"_737":855,"_738":35,"_236":967},{"_89":1042,"_737":955,"_738":35,"_236":967},{"_89":1071,"_737":852,"_738":35,"_236":1072},{"_89":1059,"_737":858,"_738":100,"_236":1060},{"_89":1069,"_737":852,"_738":35,"_236":1070},{"_89":1059,"_737":855,"_738":100,"_236":1060},{"_89":1067,"_737":954,"_738":35,"_236":1068},{"_89":1059,"_737":976,"_738":100,"_236":1060},{"_89":1039,"_737":855,"_738":35,"_236":1041},{"_89":1065,"_737":852,"_738":100,"_236":1066},{"_89":1063,"_737":852,"_738":35,"_236":1064},{"_89":1061,"_737":852,"_738":100,"_236":1062},{"_89":1059,"_737":950,"_738":100,"_236":1060},{"_89":1057,"_737":852,"_738":35,"_236":1058},{"_89":1055,"_737":852,"_738":35,"_236":1056},{"_89":1053,"_737":852,"_738":100,"_236":1054},{"_89":1051,"_737":959,"_738":35,"_236":1052},{"_89":1039,"_737":950,"_738":35,"_236":1041},{"_89":1049,"_737":852,"_738":35,"_236":1050},{"_89":1047,"_737":852,"_738":100,"_236":1048},{"_89":1045,"_737":852,"_738":35,"_236":1046},{"_89":1043,"_737":852,"_738":35,"_236":1044},{"_89":1042,"_737":852,"_738":35,"_236":967},{"_89":1039,"_737":1040,"_738":35,"_236":1041},{"_89":1037,"_737":852,"_738":35,"_236":1038},{"_89":1035,"_737":852,"_738":100,"_236":1036},{"_89":1033,"_737":852,"_738":35,"_236":1034},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. This ensures that behavior in subsequent visits to the same site will be attributed to the same user ID.","_hjHasCachedUserAttributes","This cookie name is associated with Hotjar which is used to know whether the data set in _hjUserAttributes Local Storage item is up to date or not.","s_ppv","This cookie is associated with Adobe Analytics site tracking and is used to measure a user's scroll activity to see how much of a page they view before moving to another page.","_gid","This cookie name is associated with Google Universal Analytics. This appears to be a new cookie and as of Spring 2017 no information is available from Google. It appears to store and update a unique value for each page visited.","_hjTLDTest","When the Hotjar script executes we try to determine the most generic cookie path we should use, instead of the page hostname. This is done so that cookies can be shared across subdomains (where applicable). To determine this, we try to store the _hjTLDTest cookie for different URL substring alternatives until it fails. After this check, the cookie is removed.","ekfls-2025-03-27","This cookie name is associated with readme.io which is used to help maintain data for the API reference support page.","s_getNewRepeat","This cookie is used to determine whether a visitor is a new visitor or a repeat visitor to the site, and is used for analytical purposes.","_hly_sid","This cookie is likely associated with website analytics, as the prefix '_hly_' suggests a connection to Holistics, a business intelligence and analytics platform.","docs.nvidia.com","_hjSession_xxxxxx","A cookie that holds the current session data. 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These cookies are required for the site to function and cannot be turned off.",[1221,1222,1223,1224,1225,1226,1227,1228,1229,1230,1231,1232,1233,1234,1235,1236,1237,1238,1239,1240,1241,1242,1243,1244,1245,1246,1247,1248,1249,1250,1251,1252,1253,1254,1255,1256,1257,1258,1259,1260,1261,1262,1263,1264,1265,1266,1267,1268,1269,1270,1271,1272,1273,1274,1275,1276,1277,1278,1279,1280,1281,1282,1283,1284,1285,1286,1287,1288,1289,1290,1291,1292,1293],[1081,1082,1083,1084,1085,1086,1087,1088,1089,1090,1091,1092,1093,1094,1095,1096,1097,1098,1099,1100,1101,1102,1103,1104,1105],{"_731":1218,"_554":1218,"_236":237,"_704":1219},{"_731":1212,"_554":1212,"_236":237,"_704":1213},{"_731":1209,"_554":1209,"_236":237,"_704":1210},{"_731":1197,"_554":1197,"_236":237,"_704":1198},{"_731":1194,"_554":1194,"_236":237,"_704":1195},{"_731":1191,"_554":1191,"_236":237,"_704":1192},{"_731":1186,"_554":1186,"_236":237,"_704":1187},{"_731":793,"_554":793,"_236":237,"_704":1183},{"_731":1180,"_554":1180,"_236":237,"_704":1181},{"_731":785,"_554":785,"_236":237,"_704":1178},{"_731":780,"_554":780,"_236":237,"_704":1176},{"_731":1171,"_554":1171,"_236":237,"_704":1172},{"_731":1166,"_554":1166,"_236":237,"_704":1167},{"_731":1161,"_554":1161,"_236":237,"_704":1162},{"_731":1158,"_554":1158,"_236":237,"_704":1159},{"_731":906,"_554":906,"_236":237,"_704":1155},{"_731":765,"_554":765,"_236":237,"_704":1152},{"_731":901,"_554":901,"_236":237,"_704":1145},{"_731":897,"_554":897,"_236":237,"_704":1139},{"_731":1133,"_554":1133,"_236":237,"_704":1134},{"_731":1128,"_554":1128,"_236":237,"_704":1129},{"_731":1123,"_554":1123,"_236":237,"_704":1124},{"_731":1119,"_554":1119,"_236":237,"_704":1120},{"_731":1114,"_554":1114,"_236":237,"_704":1115},{"_731":1106,"_554":1106,"_236":237,"_704":1107},"polyfill.io",[1108,1109],{"_89":1112,"_737":1106,"_738":35,"_236":1113},{"_89":1110,"_737":1106,"_738":35,"_236":1111},"cf_ob_info","This cookie is associated with sites using CloudFlare. 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The service also uses cookies for various purposes, including analytics, personalization, and advertising.","readme.io",[1130],{"_89":1131,"_737":1128,"_738":35,"_236":1132},"__cf_bm","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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