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Launch a dedicated cloud GPU server running Laboratory OS to download and run Llama 3.3 70B using any compatible app or framework.
Must be used with a compatible app, notebook, or codebase. May run slowly, or not work at all, depending on local system resources, particularly GPU(s) and available VRAM.
Open WebUI is an open-source, self-hosted web interface with a polished, ChatGPT-like user experience for interacting with LLMs. Integrates seamlessly with local Ollama installation.
The most full-featured web interface for experimenting with open source Large Language Models. Featuring a wide range of configurable settings, inference engines, and plugins.
Llama 3.3 70B is a 70-billion parameter transformer-based language model developed by Meta, featuring instruction tuning through supervised fine-tuning and reinforcement learning from human feedback. The model supports a 128,000-token context window, incorporates Grouped-Query Attention for enhanced inference efficiency, and demonstrates multilingual capabilities across eight validated languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Llama 3.3 70B Instruct is a large-scale, instruction-tuned language model released by Meta on December 6, 2024. Developed as part of the Llama 3.3 family, the model is engineered for both research and commercial applications, emphasizing multilingual support, advanced reasoning, and improved safety through robust fine-tuning methods. Built on a transformer architecture, the model exhibits capabilities in conversational contexts and performance across a range of natural language processing benchmarks, with technical details and supporting resources made available by Meta for public use and further development, as detailed in their Llama Docs Overview.
Llama 3.3 70B Instruct employs an optimized transformer-based architecture, incorporating Grouped-Query Attention (GQA) to enhance inference scalability, as outlined in the Llama Docs Overview. As an auto-regressive model, it generates text token-by-token based on input context, supporting a context window of up to 128,000 tokens. The architecture is further adapted for instruction-based interaction through supervised fine-tuning and reinforcement learning from human feedback (RLHF), aligning model outputs with preferred human responses to enhance utility and safety, as described in the Llama GitHub README.
A key feature of the Llama 3.3 70B Instruct model is its multilingual capacity. While pretraining draws on a broad array of languages, the model is explicitly validated for English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. Developers considering applications in additional languages are encouraged to perform their own safety and reliability evaluations, as recommended in the Llama Docs Overview.
Support for external tool use is built into the architecture, allowing the model to interface with APIs or functions such as weather lookups or data retrieval. This is made possible via chat templating and function calling within the Transformers framework, as detailed in Hugging Face's documentation on Advanced Tool Use. Furthermore, memory optimization is available through quantization, supporting both 8-bit and 4-bit precision for resource-constrained deployments.
Llama 3.3 70B Instruct is pretrained on approximately 15 trillion tokens sourced from publicly available datasets. Fine-tuning data includes a combination of curated human-generated instructions and over 25 million synthetic examples created to augment alignment and safety, as noted in the Llama Docs Overview. A multitiered data collection process is used, blending vendor-supplied human data with large-scale synthetic data generation and rigorous filtering via classifier models to aim to produce high-quality outputs. The model knowledge is current up to December 2023, reflecting a diverse and recent corpus.
During instruction-tuning, reinforcement learning from human feedback is extensively used. This methodology combines labeler preferences with synthetic scenarios to foster safe and helpful behaviors in generated responses, as described in the Llama Responsible Use Guide. Meta also applies automated quality control to reduce safety risks and improve performance across supported languages and applications.
The Llama 3.3 70B Instruct model has been evaluated across a suite of widely recognized benchmarks covering reasoning, mathematics, programming, multilingual understanding, and tool use. Comparative analysis with prior entries in the Llama family—including Llama 3.1 8B and Llama 3.1 70B—demonstrates specific performance metrics in accuracy, code generation, and steerability. For instance, on the MMLU (CoT) benchmark, Llama 3.3 70B achieves a macro average accuracy of 86.0%, while on HumanEval for code generation, it attains a pass@1 score of 88.4%. In multilingual math (MGSM), the model yields an exact match score of 91.1%, as documented in the Llama Docs Overview.
Other tasks, such as IFEval for steerability and BFCL v2 for tool use, reveal similar gains, indicating the model's applicability across various general and specialized AI workloads. Context length and inference optimizations further enhance practical deployment possibilities.
Llama 3.3 70B Instruct is designed for a broad spectrum of applications across commercial and research domains, as outlined in the Llama Responsible Use Guide. Its instruction-tuned design makes it particularly suitable for assistant-like chat interfaces requiring rich, extended reasoning, as well as for code generation, summarization, translation, and general-purpose natural language generation.
The model’s support for external tool calls extends its capability to agentic systems that can query databases, fetch real-time information, or interact with other software tools. Outputs from Llama 3.3 can be leveraged for synthetic data generation, dataset distillation, or further model training. Multilingual capabilities and robust alignment strategies further position the model for globally relevant deployments while enabling downstream customization for specific tasks and regions.
Despite extensive training and alignment, Llama 3.3 70B Instruct retains limitations inherent to large language models. Outputs may still contain inaccuracies, biases, or rare failures to comply with safety constraints, as acknowledged in the Llama Trust & Safety documentation. The model is static, trained on an offline dataset, and not designed for real-time adaptation or knowledge updates without further retraining. Developers are expected to conduct rigorous application-specific safety evaluations and to integrate appropriate guardrails, especially when extending support to languages or domains not natively verified by Meta.
Furthermore, as a component in larger AI systems, the model should not be deployed in isolation, and any integration with external tools or services places responsibility on the developer to define policy and assess operational risks. Additional compliance requirements apply to tool use and the integration of third-party resources, as specified in the Llama Acceptable Use Policy.
Llama 3.3 70B Instruct is released under the Llama 3.3 Community License Agreement, a custom license tailored for broad research and commercial use, as stated in the Llama 3.3 Community License. The license grants rights to use, reproduce, modify, and distribute the model, subject to attribution, compliance with law, and prominent marking for products utilizing the model (such as “Built with Llama” badging).
Redistribution and derivative model naming are subject to specific guidelines, and users with products exceeding 700 million monthly active users on the release date require a separate commercial license from Meta. The acceptable use policy prohibits illicit or harmful activities, including applications that violate safety, legal, or ethical standards. It is important to note that the license for multimodal variants includes regional restrictions, such as different terms for entities located in the European Union. No warranty or trademark rights are provided outside the explicit terms of the Llama 3.3 Acceptable Use Policy.