Llama 3.1 8B is part of Meta's latest suite of language models, designed to offer a blend of performance and efficiency.
| ✅ Availability | Yes, Llama 3.1 8B Instruct here |
| 🐙 Model Type | Large Language Model (LLM) |
| 🗓️ Release Date | October 2023 |
| 📅 Training Data Cut-off Date | N/A |
| 📏 Parameters (Size) | 8 billion |
| 🔢 Context Window | 128k tokens |
| 🌎 Supported Languages | Multiple |
| 📈 MMLU Score | 73.0% |
| 🗝️ API Availability | Yes |
| 💰 Pricing (per 1M Token) | Input: $0.09, Output: $0.09 per 1M tokens |
This model boasts significant improvements in reasoning, multilingual capabilities, and coding assistance, making it a versatile choice for a variety of applications.
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Architecture 🏗️
Llama 3.1 8B utilizes a standard decoder-only transformer architecture, optimized for stability and scalability.
The model was trained on a vast dataset of over 15 trillion tokens, leveraging 16 thousand H100 GPUs to achieve high efficiency and performance. This setup ensures that the model can handle complex tasks and large inputs with ease.
Performance 🏎️
Llama 3.1 8B has shown competitive results across a range of benchmarks, including general knowledge, math, tool use, and multilingual translation. It outperforms its predecessors and stands strong against other leading models in the market.
Key Benchmark Results:
- MMLU (CoT): 73.0
- HumanEval (0-shot): 72.6
- GSM8K (8-shot, CoT): 84.5
- ARC Challenge (0-shot): 83.4
- API-Bank (0-shot): 82.6
Pricing 💵
Llama 3.1 8B costs $0.30 for input and $0.60 for output per million tokens. (AWS)
Token Pricing
The cost structure for using Llama 3.1 8B is designed to be competitive and transparent. Token pricing is divided into input and output costs, making it easier for developers to estimate their expenses based on usage patterns.
Example Cost Calculation
To provide a practical example, let's assume a project requires processing 1 million tokens. If the input cost is $0.12 per 1,000 tokens and the output cost is $0.50 per 1,000 tokens, the total cost would be calculated as follows:
- Input Cost: (1,000,000 tokens / 1,000) * $0.12 = $120
- Output Cost: (1,000,000 tokens / 1,000) * $0.50 = $500
- Total Cost: $120 + $500 = $620
Use Cases 🗂️
Llama 3.1 8B is versatile and can be adapted for various applications:
- Long-form Text Summarization: With its 128K context length, the model can process extensive documents and generate concise summaries.
- Multilingual Conversational Agents: The model's multilingual support allows it to handle conversations in multiple languages, making it ideal for global applications.
- Coding Assistance: Llama 3.1 8B can assist in generating and debugging code, providing valuable support for developers.
Customization
One of the standout features of Llama 3.1 8B is its ability to be fine-tuned and customized for specific use cases. This allows developers to tailor the model's behavior to better suit their unique requirements, enhancing its effectiveness and efficiency.
Comparison 📊
When compared to other models in the market, such as GPT-4 and Claude 3.5 Sonnet, Llama 3.1 8B holds its ground with impressive performance metrics and a more cost-effective pricing structure.
| Feature | Llama 3.1 8B | GPT-4o mini | Claude 3 Haiku |
|---|---|---|---|
| Description | Enhanced reasoning and coding capabilities, multilingual support | Cost-efficient small model with strong performance in reasoning and coding | Compact model for quick and accurate targeted performance |
| Context Window | 128K tokens | 128K tokens | 200K tokens |
| Max Output Tokens | 4096 tokens | 16K tokens | 4096 tokens |
| Multilingual Support | Yes | Yes | Yes |
| Vision Capabilities | No | Yes | Yes |
| Reasoning Performance (MMLU) | 73.0% | 82.0% | 73.8% |
| Math Proficiency (MGSM) | 84.5% | 87.0% | 71.7% |
| Coding Performance (HumanEval) | 72.6% | 87.2% | 75.9% |
| API Model Name | llama-3.1-8b | gpt-4o-mini | claude-3-haiku |
| Input Cost per Million Tokens | $8 | $0.15 | $0.25 |
| Output Cost per Million Tokens | $24 | $0.60 | $1.25 |
| Training Data Cut-off | April 2024 | October 2023 | August 2023 |
| Fine-Tuning Availability | Yes | No | No |
| Deployment Options | Cloud, On-premises | Cloud | Cloud |
| Security Features | End-to-end encryption, GDPR compliant | Basic encryption | End-to-end encryption, GDPR compliant |
| API Rate Limits | 1000 requests/minute | 500 requests/minute | 750 requests/minute |
| Use Cases | Advanced NLP tasks, multilingual applications, enterprise solutions | Cost-effective NLP tasks, small to medium applications | Quick and accurate responses, real-time applications |
| Community Support | Strong, with active forums and developer resources | Moderate, with basic support channels | Strong, with detailed documentation and active forums |
| Unique Selling Point | High performance across various benchmarks with multilingual support | Cost-efficiency with competitive performance | Exceptional context handling and quick response times |
Its balance of performance, scalability, and affordability makes it a strong contender in the AI model landscape.
Conclusion
Llama 3.1 8B is a powerful, efficient, and versatile language model that caters to a wide range of applications.
With its robust architecture, competitive performance, and cost-effective pricing, it is an excellent choice for developers looking to leverage advanced AI capabilities in their projects.
