Model Information
Jamba Mini 1.7 offers new improvements to our Jamba open model family. This new version builds on the novel SSM-Transformer hybrid architecture, 256K context window, and efficiency gains of previous versions, while introducing improvements in grounding and instruction-following.
Key Improvements
- Grounding: Jamba Mini 1.7 provides more complete and accurate answers, grounded fully in the given context.
- Instruction following: Jamba Mini 1.7 improves on steerability.
Use Cases
Jamba’s long context efficiency, contextual faithfulness, and steerability make it ideal for a variety of business applications and industries, such as:
- Finance: Investment research, digital banking support chatbot, M&A due diligence.
- Healthcare: Procurement (RFP creation & response review), medical publication and reports generation.
- Retail: Brand-aligned product description generation, conversational AI.
- Education & Research: Personalized chatbot tutor, grants applications.
The models are released under the Jamba Open Model License, a permissive license allowing full research use and commercial use under the license terms. If you need to license the model for your needs, talk to us.
Model Details
- Developed by: AI21
- Model type: Joint Attention and Mamba (Jamba)
- License: Jamba Open Model License
- Context length: 256K
- Knowledge cutoff date: August 22nd, 2024
- Supported languages: English, Spanish, French, Portuguese, Italian, Dutch, German, Arabic and Hebrew
Grounding and instruction-following improvements
| Category | Benchmark | Jamba Mini 1.6 | Jamba Mini 1.7 |
|---|---|---|---|
| Grounding | FACTS | 0.727 | 0.790 |
| Steerability | IFEcal | 0.68 | 0.76 |
Usage
Find step-by-step instructions on how to privately deploy Jamba:
You can also find all instructions in our private AI (vLLM) deployment guide.
And to get started with our SDK:
AI21 Python SDK guide
Further Documentation
For comprehensive guides and advanced usage:
- Tokenization Guide – Using
ai21-tokenizer - Quantization Guide – ExpertsInt8, bitsandbytes
- Fine-tuning Guide – LoRA, qLoRA and full fine-tuning
For more resources to start building, visit our official documentation.
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