Gemma-1.1-7b-it is an open-source language model developed by Google.
It was trained using a novel Reinforcement Learning from Human Feedback (RLHF) method.
Scorecard
| ⛔️ Availability | No, Gemma-1.1-7b-it is a legacy model. Try Google Gemma 2.1 27B-it instead |
| 🐙 Model Type | Large Language Model (LLM) |
| 🗓️ Release Date | February 2024 |
| 📅 Training Data Cut-off Date | N/A |
| 📏 Parameters (Size) | 7 billion |
| 🔢 Context Window | 8k tokens |
| 🌎 Supported Languages | Primarily English |
| 📈 MMLU Score | 48.7%% |
| 🗝️ API Availability | Yes |
| 💰 Pricing (per 1M Token) | Input: $0.27, Output: $0.27 per 1M tokens |
Architecture 🏗️
The Gemma-1.1-7b-it model boasts a robust architecture designed for diverse applications.
It employs a transformer-based neural network with 7 billion parameters, enabling high-quality text generation, coding tasks, and more. The model is optimized for both performance and scalability, making it suitable for a wide range of use cases.
Performance 🏎️
Gemma-1.1-7b-it delivers substantial gains in various performance metrics:
- Quality: Enhanced text generation and coding capabilities.
- Factuality: Improved accuracy in providing factual information.
- Instruction Following: Better adherence to given instructions.
- Multi-turn Conversation: Superior handling of extended dialogues.
Pricing 💵
Token Pricing
The pricing for Gemma-1.1-7b-it is competitive, aimed at making high-quality AI accessible:
- Input Tokens: $0.20 per million tokens
- Output Tokens: $0.80 per million tokens
Example Cost Calculation
For a typical use case involving 1 million input tokens and 1 million output tokens, the cost would be:
- Input Cost: $0.20
- Output Cost: $0.80
- Total Cost: $1.00
Use Cases 🗂️
Gemma-1.1-7b-it can be applied to various domains, including but not limited to:
- Text Generation: Crafting high-quality content for blogs, articles, and creative writing.
- Coding Assistance: Generating code snippets, debugging, and providing coding suggestions.
- Customer Support: Automating responses to customer queries with high accuracy.
- Data Analysis: Interpreting and summarizing complex datasets.
Customization
The model allows for fine-tuning to adapt to specific applications, improving its performance in niche areas. This customization can be achieved through additional training on domain-specific data.
Comparison 📊
When compared to other models, Gemma-1.1-7b-it stands out due to its balanced approach to quality and cost-efficiency. It competes closely with models like OpenAI's GPT-3.5 Turbo and Anthropic's Claude, offering a similar level of performance at a more affordable rate.
Conclusion
Gemma-1.1-7b-it is a versatile and powerful language model that excels in various applications, from text generation to coding assistance. Its competitive pricing makes it an attractive option for businesses and developers looking to leverage AI technology without breaking the bank.
