AI21 Jamba 1.5 Large
Version: 1
Jamba 1.5 Large is a state-of-the-art, hybrid SSM-Transformer instruction following foundation model. It's a Mixture-of-Expert model with 94B total parameters and 398B active parameters. The Jamba family of models are the most powerful & efficient long-context models on the market, offering a 256K context window, the longest available.. For long context input, they deliver up to 2.5X faster inference than leading models of comparable sizes. Jamba supports function calling/tool use, structured output (JSON), and grounded generation with citation mode and documents API. Jamba officially supports English, French, Spanish, Portuguese, German, Arabic and Hebrew, but can also work in many other languages.
Model Developer Name: Jamba 1.5 Large
Model Architecture
Jamba 1.5 Large is a state-of-the-art, hybrid SSM-Transformer instruction following foundation modelModel Variations
94B total parameters and 398B active parametersModel Input
Models input text only.Model Output
Models generate text only.Model Dates
Jamba 1.5 Large was trained in Q3 2024 with data covering through early March 2024.Model Information Table
| Name | Params | Content Length |
|---|---|---|
| Jamba 1.5 Mini | 52B (12B active) | 256K |
| Jamba 1.5 Large | 398B (94B active) | 256K |
License
Please refer to this link for AI21's Terms of Use For more information about how Azure processes data, for privacy and security in relation to Models-as-a-Service (MaaS), please see this Microsoft Docs link .Ethical Considerations and Limitations
AI21 Labs is on a mission to make AI-first experiences, with machines working alongside humans as thought partners, thereby promoting human welfare and prosperity. To deliver its promise, this technology must be deployed and used in a responsible and sustainable way, taking into consideration potential risks, including malicious use by bad actors, accidental misuse and broader societal harms. We take these risks extremely seriously and put measures in place to mitigate them. AI safety is an important challenge with a large surface area, which we believe can be addressed most effectively by working together. We invite anyone interested in conducting research or otherwise promoting AI safety to contact us at safety@ai21.com and explore opportunities for collaboration.Content Filtering
Prompts and completions in GitHub Models are passed through a default configuration of Azure AI Content Safety classification models to detect and prevent the output of harmful content. Learn more about Azure AI Content Safety . Configuration options for content filtering vary when you deploy a model for production in Azure AI; learn more .Training Data
Jamba is trained on an in-house dataset that contains text data from the web, books, and code. The knowledge cutoff date is March 5, 2024.Evaluation Results
| Category | Metric | Score |
|---|---|---|
| General | Arena Hard | 65.4 |
| MMLU (CoT) | 81.2 | |
| MMLU Pro (CoT) | 53.5 | |
| IFEval | 81.5 | |
| BBH | 65.5 | |
| WildBench | 48.4 | |
| Reasoning | ARC-C | 93 |
| GPQA | 36.9 | |
| Math, Code & Tool use | GSM8K | 87 |
| HumanEval | 71.3 | |
| BFCL | 85.5 |
Evaluation of pretrained LLMs on automatic safety benchmarks*
| TruthfulQA | |
|---|---|
| Jamba 1.5 Mini | 54.1 |
| Jamba 1.5 Large | 58.3 |
Evaluation of fine-tuned LLMs on different safety datasets*
| RealToxicity* | |
|---|---|
| Jamba 1.5 Mini | 8.1 |
| Jamba 1.5 Large | 6.7 |
Model Specifications
Context Length262144
LicenseCustom
Last UpdatedOctober 2024
Input TypeText
Output TypeText
PublisherAI21 Labs
Languages7 Languages