Llama 3 Instruct 70B
Achieve excellence in multilingual AI reasoning and content generation while saving significantly.
Llama 3 Instruct (70B) from Meta is a powerhouse for various applications, from language reasoning to game development and content creation. This model outperforms many leading closed-source models, making it a versatile tool for developers and content creators alike.
| License | llama3 |
|---|---|
| Context window(in thousands) | 8192 |
Use cases for Llama 3 Instruct 70B
- Language reasoning tasks: Llama 3 Instruct (70B) excels in language reasoning, even in non-native languages, making it ideal for multilingual AI projects.
- Game development: The model’s ability to craft a flawless snake game showcases its potential in game development and simulations.
- Content creation and summarization: With strong conversational abilities and the capability to condense large texts, this model is perfect for creating and summarizing content.
| Arena Elo | 1206 |
|---|---|
| MMLU | 82 |
| MT Bench | N/A |
Llama 3 Instruct (70B) stands out in knowledge and reasoning tasks, scoring high in human-based quality evaluations and translation benchmarks.
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1206
1186
1165
| Throughput(output tokens per second) | 41 |
|---|---|
| Latency(seconds to first tokens chunk received) | 0.29 |
| Total Response Time(seconds to output 100 tokens) | 2.8 |
This model offers moderate throughput, suitable for applications with moderate user concurrency. Its low latency makes it great for real-time interactions. However the total response time is slower, which may not the best choice for applications needing immediate responses.
The cost per 1,000 tokens for utilizing the model with Telnyx Inference stands at $0.0010. To provide a perspective, analyzing 1,000,000 customer chats, presuming each chat is 1,000 tokens long, would cost $1,000.
What's Twitter saying?
- Top-5 Leaderboard Achievement for Open-Weights Model: Llama 3 70B Instruct ranks alongside Gemini Pro and Claude 3 Sonnet, achieving a top-5 spot on the Arena leaderboard with over 12K votes. (Source: @simonw)
- LLM Comparison and Key Findings: A Reddit post highlights Llama 3 Instruct 70B's performance with quantization, recommending EXL2 and GGUF formats. (Source: @rohanpaul_ai)
- Fine-Tuning Experiment Observations: Philipp Schmid’s experiments with fine-tuning Llama 3 8B (70B) using Q-LoRA reveal challenges with special token training, suggesting the Vicuna format or extending the model for ChatML. (Source: @_philschmid)
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What is Llama-3-70B-Instruct?
Llama-3-70B-Instruct is part of the Meta Llama 3 family, a large language model with 70 billion parameters designed for various tasks, including dialogue. It features a decoder-only transformer architecture and is pretrained on a dataset of over 15 trillion tokens for superior performance in multilingual support, efficiency, and versatility in tasks like coding, trivia, and creative writing.
How does Llama-3-70B-Instruct compare to GPT models?
Llama-3-70B-Instruct is reported to be comparable to GPT-4 in performance, excelling in areas such as email chain summarization and coding. It establishes a new state-of-the-art for large language models, outperforming other open-source chat models on common benchmarks.
What makes Llama-3-70B-Instruct efficient?
The model uses Grouped-Query Attention (GQA) across both its 8B and 70B versions, which ensures improved inference efficiency and scalability. This technique optimizes the model for faster and more efficient performance during tasks.
Can Llama-3-70B-Instruct handle tasks in languages other than English?
Yes, while currently optimized for English, Llama-3-70B-Instruct includes a significant amount of non-English data in its pretraining dataset. This makes it versatile for multilingual use cases and increases its future potential for global applications.
How is Llama-3-70B-Instruct fine-tuned for better performance?
Llama-3-70B-Instruct undergoes Supervised Fine-tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) to align more closely with human preferences for helpfulness and safety. This process ensures that the model is better suited to provide valuable and safe interactions.
What are some use cases for Llama-3-70B-Instruct?
Llama-3-70B-Instruct is designed to perform well across a variety of tasks, including trivia questions, STEM fields, coding, historical knowledge, and creative writing. Its versatility makes it suitable for a wide range of applications in different industries.
How does community feedback influence Llama-3-70B-Instruct development?
The development team behind Llama-3-70B-Instruct values community feedback highly, using it to refine the model's performance and safety over time. Future versions of the tuned models will be released as improvements are made based on this feedback.
Where can I use Llama-3-70B-Instruct for my projects?
Users can integrate Llama-3-70B-Instruct into their connectivity apps through platforms like Telnyx. This allows developers to leverage the model's capabilities for a wide range of applications, from customer service chatbots to more complex AI-driven solutions. For more information on how to start building with Llama-3-70B-Instruct on Telnyx, visit Telnyx's developer documentation.
Is Llama-3-70B-Instruct suitable for creative writing and content generation?
Yes, Llama-3-70B-Instruct excels in creative writing and content generation tasks. Its large dataset and sophisticated architecture allow it to generate high-quality, creative text outputs, making it a valuable tool for writers, marketers, and content creators.