The Meta Llama 3.3 multilingual large language model (LLM) is an instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks.
Model developer: Meta
Model Architecture: Llama 3.3 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.
Llama 3.3 model. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
Model Release Date:
70B Instruct: December 6, 2024
Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3.3 in applications, please go here.
Intended Use
Intended Use Cases Llama 3.3 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases.
Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.3 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**Note: Llama 3.3 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.3 models for languages beyond the 8 supported languages provided they comply with the Llama 3.3 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.3 in additional languages is done in a safe and responsible manner.
How to use
This repository contains two versions of Llama-3.3-70B-Instruct, for use with transformers and with the original llama codebase.
Deployment
This model can be deployed efficiently on vLLM, Red Hat Enterprise Linux AI, and Openshift AI, as shown in the example below.
Deploy on vLLM
python
1from vllm import LLM, SamplingParams
23from transformers import AutoTokenizer
45model_id ="RedHatAI/Llama-3.3-70B-Instruct"6number_gpus =478sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)910tokenizer = AutoTokenizer.from_pretrained(model_id)1112prompt ="Give me a short introduction to large language model."1314llm = LLM(model=model_id, tensor_parallel_size=number_gpus)1516outputs = llm.generate(prompt, sampling_params)1718generated_text = outputs[0].outputs[0].text
19print(generated_text)
vLLM also supports OpenAI-compatible serving. See the documentation for more details.
1# Download model from Red Hat Registry via docker2# Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified.3ilab model download --repository docker://registry.redhat.io/rhelai1/llama-3-3-70b-instruct:1.5
bash
1# Serve model via ilab2ilab model serve --model-path ~/.cache/instructlab/models/llama-3-3-70b-instruct
34# Chat with model5ilab model chat --model ~/.cache/instructlab/models/llama-3-3-70b-instruct
1# Setting up vllm server with ServingRuntime2# Save as: vllm-servingruntime.yaml3apiVersion: serving.kserve.io/v1alpha1
4kind: ServingRuntime
5metadata:6 name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name7 annotations:8 openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe
9 opendatahub.io/recommended-accelerators:'["nvidia.com/gpu"]'10 labels:11 opendatahub.io/dashboard:'true'12spec:13 annotations:14 prometheus.io/port:'8080'15 prometheus.io/path:'/metrics'16 multiModel: false
17 supportedModelFormats:18- autoSelect: true
19 name: vLLM
20 containers:21- name: kserve-container
22 image: quay.io/modh/vllm:rhoai-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-rocm23 command:24- python
25--m
26- vllm.entrypoints.openai.api_server
27 args:28-"--port=8080"29-"--model=/mnt/models"30-"--served-model-name={{.Name}}"31 env:32- name: HF_HOME
33 value:/tmp/hf_home
34 ports:35- containerPort:808036 protocol: TCP
python
1# Attach model to vllm server. This is an NVIDIA template2# Save as: inferenceservice.yaml3apiVersion: serving.kserve.io/v1beta1
4kind: InferenceService
5metadata:6 annotations:7 openshift.io/display-name: llama-3-3-70b-instruct # OPTIONAL CHANGE8 serving.kserve.io/deploymentMode: RawDeployment
9 name: llama-3-3-70b-instruct # specify model name. This value will be used to invoke the model in the payload10 labels:11 opendatahub.io/dashboard:'true'12spec:13 predictor:14 maxReplicas:115 minReplicas:116 model:17 modelFormat:18 name: vLLM
19 name:''20 resources:21 limits:22 cpu:'2'# this is model specific23 memory: 8Gi # this is model specific24 nvidia.com/gpu:'1'# this is accelerator specific25 requests:# same comment for this block26 cpu:'1'27 memory: 4Gi
28 nvidia.com/gpu:'1'29 runtime: vllm-cuda-runtime # must match the ServingRuntime name above30 storageUri: oci://registry.redhat.io/rhelai1/modelcar-llama-3-3-70b-instruct:1.531 tolerations:32- effect: NoSchedule
33 key: nvidia.com/gpu
34 operator: Exists
bash
1# make sure first to be in the project where you want to deploy the model2# oc project <project-name>34# apply both resources to run model56# Apply the ServingRuntime7oc apply -f vllm-servingruntime.yaml
89# Apply the InferenceService10oc apply -f qwen-inferenceservice.yaml
python
1# Replace <inference-service-name> and <cluster-ingress-domain> below:2# - Run `oc get inferenceservice` to find your URL if unsure.34# Call the server using curl:5curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions
6-H "Content-Type: application/json" \
7-d '{8"model":"llama-3-3-70b-instruct",9"stream": true,10"stream_options":{11"include_usage": true
12},13"max_tokens":1,14"messages":[15{16"role":"user",17"content":"How can a bee fly when its wings are so small?"18}19]20}'
21
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:
python
1import transformers
2import torch
34model_id ="meta-llama/Llama-3.3-70B-Instruct"56pipeline = transformers.pipeline(7"text-generation",8 model=model_id,9 model_kwargs={"torch_dtype": torch.bfloat16},10 device_map="auto",11)1213messages =[14{"role":"system","content":"You are a pirate chatbot who always responds in pirate speak!"},15{"role":"user","content":"Who are you?"},16]1718outputs = pipeline(19 messages,20 max_new_tokens=256,21)22print(outputs[0]["generated_text"][-1])
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:
python
1# First, define a tool2defget_current_temperature(location:str)->float:3"""
4 Get the current temperature at a location.
56 Args:
7 location: The location to get the temperature for, in the format "City, Country"
8 Returns:
9 The current temperature at the specified location in the specified units, as a float.
10 """11return22.# A real function should probably actually get the temperature!1213# Next, create a chat and apply the chat template14messages =[15{"role":"system","content":"You are a bot that responds to weather queries."},16{"role":"user","content":"Hey, what's the temperature in Paris right now?"}17]1819inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
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:
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:
python
1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
34model_id ="meta-llama/Llama-3.3-70B-Instruct"5quantization_config = BitsAndBytesConfig(load_in_8bit=True)67quantized_model = AutoModelForCausalLM.from_pretrained(8 model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)910tokenizer = AutoTokenizer.from_pretrained(model_id)11input_text ="What are we having for dinner?"12input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")1314output = quantized_model.generate(**input_ids, max_new_tokens=10)1516print(tokenizer.decode(output[0], skip_special_tokens=True))
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:
Training Factors We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
Training Energy Use Training utilized a cumulative of 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
Training Greenhouse Gas Emissions Estimated total location-based greenhouse gas emissions were 11,390 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
Training Time (GPU hours)
Training Power Consumption (W)
Training Location-Based Greenhouse Gas Emissions (tons CO2eq)
Training Market-Based Greenhouse Gas Emissions (tons CO2eq)
Llama 3.3 70B
7.0M
700
2,040
0
The methodology used to determine training energy use and greenhouse gas emissions can be found here. Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
Training Data
Overview: Llama 3.3 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
Data Freshness: The pretraining data has a cutoff of December 2023.
Benchmarks - English Text
In this section, we report the results for Llama 3.3 relative to our previous models.
Instruction tuned models
Category
Benchmark
# Shots
Metric
Llama 3.1 8B Instruct
Llama 3.1 70B Instruct
Llama-3.3 70B Instruct
Llama 3.1 405B Instruct
MMLU (CoT)
0
macro_avg/acc
73.0
86.0
86.0
88.6
MMLU Pro (CoT)
5
macro_avg/acc
48.3
66.4
68.9
73.3
Steerability
IFEval
80.4
87.5
92.1
88.6
Reasoning
GPQA Diamond (CoT)
0
acc
31.8
48.0
50.5
49.0
Code
HumanEval
0
pass@1
72.6
80.5
88.4
89.0
MBPP EvalPlus (base)
0
pass@1
72.8
86.0
87.6
88.6
Math
MATH (CoT)
0
sympy_intersection_score
51.9
68.0
77.0
73.8
Tool Use
BFCL v2
0
overall_ast_summary/macro_avg/valid
65.4
77.5
77.3
81.1
Multilingual
MGSM
0
em
68.9
86.9
91.1
91.6
Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
Provide protections for the community to help prevent the misuse of our models.
Responsible deployment
Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our Community Stories webpage. Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.3 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the Responsible Use Guide to learn more.
Llama 3.3 instruct
Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.
Fine-tuning data
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
Refusals and Tone
Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
Llama 3.3 systems
Large language models, including Llama 3.3, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.
As part of our responsible release approach, we provide the community with safeguards that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our reference implementations demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
Capability specific considerations
Tool-use: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
Multilinguality: Llama 3.3 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
Red teaming
For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.
We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. .
Critical and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness
To assess risks related to proliferation of chemical and biological weapons of the Llama 3 family of models, we performed uplift testing designed to assess whether use of the Llama 3 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
2. Child Safety
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
3. Cyber attack enablement
Our cyber attack uplift study investigated whether the Llama 3 family of LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.
We also set up the Llama Impact Grants program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found here.
Finally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.
Ethical Considerations and Limitations
The core values of Llama 3.3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3.3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.3’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 any applications of Llama 3.3 model, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our Responsible Use Guide, Trust and Safety solutions, and other resources to learn more about responsible development.
