Llama-3.1-70B-Instruct Overview

Description:

Llama-3.1-70B-Instruct is a multilingual large language model from the Meta Llama 3.1 collection of pretrained and instruction-tuned generative models. This model is optimized for multilingual dialogue use cases and outperforms many available open-source and closed-source chat models on common industry benchmarks. Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture; the tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

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 meta-llama/Llama-3.1-70B-Instruct.

License/Terms of Use:

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement.

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

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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:

Developers, AI researchers, and businesses would be expected to use this system to build and power a wide range of applications that require advanced reasoning, instruction-following, and multilingual dialogue capabilities. Specific applications include creating sophisticated chatbots and virtual assistants, developing powerful content creation and summarization tools, building complex question-answering systems, and powering multilingual customer support platforms.

Release Date:

Build.NVIDIA.com 07/23/2024 via
\nllama-3.1-70b-instruct Model by Meta | NVIDIA NIM

Github 07/23/2024 via
\nhttps://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE

Huggingface 07/23/2024 via
\nhttps://huggingface.co/meta-llama/Llama-3.1-70B-Instruct

Reference(s):

https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct

Model Architecture:

Architecture Type: Transformer
\nNetwork Architecture: Llama-3.1-70B

This model was developed based on Meta-Llama-3.1-70B
\nhttps://huggingface.co/meta-llama/Llama-3.1-70B-Instruct

Number of model parameters: 7.06*10^10

Input:

Input Type(s): Text

Input Format(s): String

Input Parameters: One-Dimensional (1D)

Other Properties Related to Input: The model accepts a string of text which is converted into tokens using the model's specific tokenizer. The total length of the input prompt and the generated output cannot exceed the model's context window of 128,000 tokens.

Output:

Output Type(s): Text

Output Format(s): String

Output Parameters: One-Dimensional (1D)

Other Properties Related to Output: The model generates a string of text, produced token by token. The maximum length of the output is limited by the model's 128,000-token context window, less the length of the input prompt. Post-processing is required to decode the model's token-based output into a readable string.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine: vLLM, TensorRT

Supported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
\nNVIDIA Blackwell
\nNVIDIA Hopper
\nNVIDIA Lovelace

Preferred Operating System(s):

Linux
\nWindows

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-3.1-70B-Instruct

Usage

Use with transformers

Starting with transformers >= 4.45.0 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.

See the snippet below for usage with Transformers:

import transformers\nimport torch\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-70B-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

Tool use with transformers

LLaMA-3.3 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.

Use with bitsandbytes

The model checkpoints can be used in 8-bit and 4-bit for further memory optimisations using bitsandbytes and transformers

See the snippet below for usage:

import torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\nquantization_config = BitsAndBytesConfig(load_in_8bit=True)\n\nquantized_model = AutoModelForCausalLM.from_pretrained(\n    model_id, device_map=\"auto\", torch_dtype=torch.bfloat16, quantization_config=quantization_config)\n\ntokenizer = AutoTokenizer.from_pretrained(model_id)\ninput_text = \"What are we having for dinner?\"\ninput_ids = tokenizer(input_text, return_tensors=\"pt\").to(\"cuda\")\n\noutput = quantized_model.generate(**input_ids, max_new_tokens=10)\n\nprint(tokenizer.decode(output[0], skip_special_tokens=True))\n

To load in 4-bit simply pass load_in_4bit=True

Use with llama

Please, follow the instructions in the repository.

To download Original checkpoints, see the example command below leveraging huggingface-cli:

huggingface-cli download meta-llama/Meta-Llama-3.1-70B-Instruct --include \"original/*\" --local-dir Meta-Llama-3.1-70B-Instruct\n

Training, Testing, and Evaluation Datasets:

Training Dataset

Data Modality: Text

Link: Undisclosed

Data Collection Method: Hybrid: Human, Synthetic, Automated

Labeling Method: Hybrid: Automated, Human

Properties:

The pretraining dataset contains over 15 trillion tokens. It is a multilingual dataset covering over 30 languages and was filtered heavily for quality using various techniques, including heuristic filters, NSFW filters, and text classifiers. The model's knowledge was trained on data with a cutoff of December 2023.

Testing Dataset

Link: Undisclosed

Data Collection Method: Hybrid: Human, Synthetic, Automated

Labeling Method: Hybrid: Automated, Human

Properties:

Description:

The model was tested on a diverse set of evaluation data.

Public Benchmarks: These test a wide range of capabilities, from general knowledge and reasoning (MMLU, HellaSwag) to expert-level problem-solving (GPQA) and programming (HumanEval, which contains 164 programming problems).
Internal Evaluation Set: Meta created a new high-quality test set of 2,000 prompts covering 12 key use cases (e.g., coding, reasoning, creative writing, instruction following). This set is used for human evaluation to assess performance on real-world, nuanced tasks.

Evaluation Dataset

Link: Undisclosed

Data Collection Method: Hybrid: Automated, Human, Synthetic

Labeling Method: Hybrid: Human, Automated

Properties:

The evaluation datasets are diverse and test a wide spectrum of capabilities. MMLU measures broad multitask knowledge. GPQA assesses advanced reasoning with difficult, expert-level questions. HumanEval and MATH specifically test code generation and mathematical reasoning abilities, respectively. Meta also utilizes a large, private, human-annotated evaluation set designed to assess model performance in real-world, nuanced scenarios.

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

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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(trtllmapi-pt-runtime-params-h200-nvlx1-throughput-bf16-gjlpxyjp3w)","nim/meta/llama-3.1-70b-instruct","key","nim/meta/models/llama-3.1-70b-instruct","resourceId","llama-3.1-70b-instruct","shortDescription","The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out).","# **Llama-3.1-70B-Instruct Overview**\n\n## **Description:**\n\n**Llama-3.1-70B-Instruct** is a multilingual large language model from the Meta Llama 3.1 collection of pretrained and instruction-tuned generative models. This model is optimized for multilingual dialogue use cases and outperforms many available open-source and closed-source chat models on common industry benchmarks. Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture; the tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.\n\nThis model is ready for commercial/non-commercial use.\n\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 [meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct). \n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** Use of this model is governed by the [NVIDIA Open Model License Agreement](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_3/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\nDevelopers, AI researchers, and businesses would be expected to use this system to build and power a wide range of applications that require advanced reasoning, instruction-following, and multilingual dialogue capabilities. Specific applications include creating sophisticated chatbots and virtual assistants, developing powerful content creation and summarization tools, building complex question-answering systems, and powering multilingual customer support platforms.\n\n## **Release Date:**\n\nBuild.NVIDIA.com 07/23/2024 via \n[llama-3.1-70b-instruct Model by Meta | NVIDIA NIM](https://build.nvidia.com/meta/llama-3_1-70b-instruct)\n\nGithub 07/23/2024 via \n[https://github.com/meta-llama/llama-models/blob/main/models/llama3\\_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)\n\nHuggingface 07/23/2024 via \n[https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)\n\n## **Reference(s):** \n\n[https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)\n\n## **Model Architecture:** \n\nArchitecture Type: Transformer \nNetwork Architecture: Llama-3.1-70B\n\nThis model was developed based on Meta-Llama-3.1-70B \n[https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)\n\nNumber of model parameters: 7.06*10^10\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 model accepts a string of text which is converted into tokens using the model's specific tokenizer. The total length of the input prompt and the generated output cannot exceed the model's context window of 128,000 tokens.\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 model generates a string of text, produced token by token. The maximum length of the output is limited by the model's 128,000-token context window, less the length of the input prompt. Post-processing is required to decode the model's token-based output into a readable string.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\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\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-3.1-70B-Instruct
\n\n## **Usage**\n\n**Use with transformers**\n\nStarting with transformers \\>= 4.45.0 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\nSee the snippet below for usage with Transformers:\n\n```\nimport transformers\nimport torch\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-70B-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\n**Tool use with transformers**\n\nLLaMA-3.3 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**Use with bitsandbytes**\n\nThe model checkpoints can be used in 8-bit and 4-bit for further memory optimisations using bitsandbytes and transformers\n\nSee the snippet below for usage:\n\n```\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\nquantization_config = BitsAndBytesConfig(load_in_8bit=True)\n\nquantized_model = AutoModelForCausalLM.from_pretrained(\n model_id, device_map=\"auto\", torch_dtype=torch.bfloat16, quantization_config=quantization_config)\n\ntokenizer = AutoTokenizer.from_pretrained(model_id)\ninput_text = \"What are we having for dinner?\"\ninput_ids = tokenizer(input_text, return_tensors=\"pt\").to(\"cuda\")\n\noutput = quantized_model.generate(**input_ids, max_new_tokens=10)\n\nprint(tokenizer.decode(output[0], skip_special_tokens=True))\n```\n\nTo load in 4-bit simply pass load\\_in\\_4bit=True\n\n**Use with llama**\n\nPlease, follow the instructions in the [repository](https://github.com/meta-llama/llama).\n\nTo download Original checkpoints, see the example command below leveraging huggingface-cli:\n\n```\nhuggingface-cli download meta-llama/Meta-Llama-3.1-70B-Instruct --include \"original/*\" --local-dir Meta-Llama-3.1-70B-Instruct\n```\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: Automated, Human\n\n**Properties:** \n\nThe pretraining dataset contains over 15 trillion tokens. It is a multilingual dataset covering over 30 languages and was filtered heavily for quality using various techniques, including heuristic filters, NSFW filters, and text classifiers. The model's knowledge was trained on data with a cutoff of December 2023\\. \n\n### **Testing Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method:** Hybrid: Automated, Human\n\n**Properties:** \n\n**Description:**\n\nThe model was tested on a diverse set of evaluation data.\n\n* Public Benchmarks: These test a wide range of capabilities, from general knowledge and reasoning (MMLU, HellaSwag) to expert-level problem-solving (GPQA) and programming (HumanEval, which contains 164 programming problems). \n* Internal Evaluation Set: Meta created a new high-quality test set of 2,000 prompts covering 12 key use cases (e.g., coding, reasoning, creative writing, instruction following). This set is used for human evaluation to assess performance on real-world, nuanced tasks. \n\n### **Evaluation Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Automated, Human, Synthetic\n\n**Labeling Method:** Hybrid: Human, Automated\n\n**Properties:** \n\nThe evaluation datasets are diverse and test a wide spectrum of capabilities. MMLU measures broad multitask knowledge. GPQA assesses advanced reasoning with difficult, expert-level questions. HumanEval and MATH specifically test code generation and mathematical reasoning abilities, respectively. Meta also utilizes a large, private, human-annotated evaluation set designed to assess model performance in real-world, nuanced scenarios. \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\nTesting 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 80GB
\n* A100 SXM 40GB
\n* L40S PCIe
\n* A10G
\n* H100 NVL
\n* H200 NVL
\n* GH200 96GB
\n* GB200 NVL72
\n* GH200 NVL2
\n* RTX 6000 Pro BSE
\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-3.1-70b-instruct",["D",1768516424290],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nim/meta/models/llama-3.1-70b-instruct","rawImageUrl","https://assets.ngc.nvidia.com/products/api-catalog/images/llama-3_1-70b-instruct.jpg","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[604,605],"displayLabels",[584,585,586,587,588],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_553":602,"_590":602,"_591":603,"_593":594},{"_553":600,"_590":600,"_591":601,"_593":594},{"_553":598,"_590":598,"_591":599,"_593":594},{"_553":596,"_590":596,"_591":597,"_593":594},{"_553":589,"_590":589,"_591":592,"_593":594},"NSPECT-7S3F-QFG8","value","filter",{"_595":594,"_590":589},"category","label","field","NIM",{"_595":594,"_590":596},"Llama3.1-70b-instruct",{"_595":594,"_590":598},"Llama3.1",{"_595":594,"_590":600},"False",{"_595":594,"_590":602},{"_553":611,"_590":611,"_591":612,"_593":609},{"_553":606,"_590":607,"_591":608,"_593":609},"NVIDIA NIM","nimmcro_nvidia_nim",{"_595":610,"_590":607},"feature","nvidia_nim","NVIDIA AI Enterprise Supported",{"_595":594,"_590":611},{"_175":1787,"_177":1788},"ngcApps",[1740],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1721":1722,"_1723":1724,"_1725":1726,"_1727":1728,"_1729":624,"_206":648,"_1730":1731,"_1732":60,"_1733":98,"_1734":98,"_1735":1736,"_1737":98,"_1738":1739},"amplitude",{"_1718":1719,"_1321":-7,"_1720":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1712":1713,"_1714":1715,"_1716":1717},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1596":236,"_1597":1598,"_1599":1600,"_1601":1602},"announcements",[],"consentState",{"_687":1586,"_1587":35,"_1588":35,"_1589":98,"_1590":1591},"consentConfig",{"_669":670,"_671":672,"_673":674,"_675":676,"_677":678,"_679":680,"_681":682,"_683":684,"_685":686,"_687":688},"showContactAdmin",1783979228117,[],[],[662,663,664,665,666,667],{"_49":63,"_51":73,"_53":170,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":69,"_53":170,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":170,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":170,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":668,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60},7,"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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They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[838,839,840,841,842,843,844,845,846,847,848,849,850,851,852,853,854,855,856,857,858,859,860,861,862,863,864,865,866,867,868,869,870,871,872],[711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727],{"_728":834,"_553":834,"_235":236,"_701":835},{"_728":829,"_553":829,"_235":236,"_701":830},{"_728":825,"_553":825,"_235":236,"_701":826},{"_728":820,"_553":820,"_235":236,"_701":821},{"_728":815,"_553":815,"_235":236,"_701":816},{"_728":803,"_553":803,"_235":236,"_701":804},{"_728":791,"_553":791,"_235":236,"_701":792},{"_728":786,"_553":786,"_235":236,"_701":787},{"_728":775,"_553":775,"_235":236,"_701":776},{"_728":770,"_553":770,"_235":236,"_701":771},{"_728":765,"_553":765,"_235":236,"_701":766},{"_728":760,"_553":760,"_235":236,"_701":761},{"_728":755,"_553":755,"_235":236,"_701":756},{"_728":750,"_553":750,"_235":236,"_701":751},{"_728":745,"_553":745,"_235":236,"_701":746},{"_728":738,"_553":738,"_235":236,"_701":739},{"_728":729,"_553":729,"_235":236,"_701":730},"hostName","amazon-adsystem.com",[731,732],{"_87":737,"_734":729,"_735":35,"_235":736},{"_87":733,"_734":729,"_735":35,"_235":736},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[740,741],{"_87":744,"_734":738,"_735":35,"_235":743},{"_87":742,"_734":738,"_735":35,"_235":743},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[767],{"_87":768,"_734":765,"_735":35,"_235":769},"sp","Testing","google.com",[772],{"_87":773,"_734":770,"_735":35,"_235":774},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. 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.","tags.srv.stackadapt.com",[777,778,779,780],{"_87":785,"_734":775,"_735":35,"_235":782},{"_87":784,"_734":775,"_735":35,"_235":782},{"_87":783,"_734":775,"_735":35,"_235":782},{"_87":781,"_734":775,"_735":35,"_235":782},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[788],{"_87":789,"_734":786,"_735":35,"_235":790},"bcookie","This domain is owned by LinkedIn, the business networking platform. 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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.",[960,961,962,963,964,965,966,967,968,969,970,971,972,973,974,975,976,977,978,979,980],[925,926,927,928,929],{"_728":820,"_553":820,"_235":236,"_701":951},{"_728":786,"_553":786,"_235":236,"_701":946},{"_728":939,"_553":939,"_235":236,"_701":940},{"_728":935,"_553":935,"_235":236,"_701":936},{"_728":930,"_553":930,"_235":236,"_701":931},"nvcrm.my.site.com",[932],{"_87":933,"_734":930,"_735":35,"_235":934},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[937],{"_87":938,"_734":935,"_735":35,"_235":769},"edge","github.com",[941,942],{"_87":945,"_734":939,"_735":35,"_235":944},{"_87":943,"_734":939,"_735":35,"_235":944},"logged_in","This domain is owned by Github. 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