💼 productive forces

AI21-Jamba-Large-1.6

AI21 Jamba Large 1.6 is a powerful hybrid SSM-Transformer architecture basic model that is good at long text processing and efficient reasoning.

#Multi-language support
#Long text processing
#Efficient reasoning
#Enterprise applications
#Follow instructions
AI21-Jamba-Large-1.6

Product Details

AI21-Jamba-Large-1.6 is a hybrid SSM-Transformer architecture base model developed by AI21 Labs, designed for long text processing and efficient reasoning. The model performs well in long text processing, reasoning speed and quality, supports multiple languages, and has strong instruction following capabilities. It is suitable for enterprise-level applications that need to process large amounts of text data, such as financial analysis, content generation, etc. The model is licensed under the Jamba Open Model License, which allows research and commercial use under the terms of the license.

Main Features

1
Supports long text processing (context length up to 256K), suitable for processing long documents and complex tasks
2
The inference speed is fast, 2.5 times faster than similar models, significantly improving efficiency
3
Supports multiple languages, including English, Spanish, French, etc., suitable for multi-language application scenarios
4
It has the ability to follow instructions and can generate high-quality text according to user instructions.
5
Supports tool calling and can be combined with external tools to expand model functions

How to Use

1
1. Install necessary dependencies, such as mamba-ssm, causal-conv1d and vllm (vllm is recommended for efficient inference).
2
2. Use vllm to load the model and set the appropriate quantization strategy (such as ExpertsInt8) to adapt to GPU resources.
3
3. Use the transformers library to load the model and combine it with bitsandbytes for quantization to optimize inference performance.
4
4. Prepare the input data and use AutoTokenizer to encode the text.
5
5. Call the model to generate text, and control the generation results by setting parameters (such as temperature, maximum generation length).
6
6. Decode the generated text and extract the content output by the model.
7
7. If you need to use the tool call function, embed the tool definition into the input template and process the tool call results returned by the model.

Target Users

This model is suitable for enterprises and developers who need to efficiently process long text data, such as finance, law, content creation and other fields. It can quickly generate high-quality text, support multi-language and complex task processing, and is suitable for business applications that require high performance and efficiency.

Examples

In the financial field, it is used to analyze and generate financial reports, providing accurate market forecasts and investment recommendations.

In content creation, it helps generate articles, stories or creative copywriting to improve creation efficiency.

In customer service scenarios, it serves as a chatbot to answer user questions and provide accurate and natural language responses.

Quick Access

Visit Website →

Categories

💼 productive forces
› Model training and deployment
› content generation

Related Recommendations

Discover more similar quality AI tools

Fume

Fume

Fume is an AI testing tool that uses artificial intelligence technology to provide users with a worry-free AI testing experience. It can generate and maintain Playwright end-to-end browser tests based on users' recorded videos, greatly simplifying the testing process and improving testing efficiency.

Automated testing QA automation
💼 productive forces
Relyable

Relyable

Relyable is an automated AI agent testing and monitoring tool that helps users evaluate, optimize and monitor the performance of AI voice agents through simulation and intelligent analysis. It helps users quickly deploy production-ready AI agents and improve work efficiency.

AI monitor
💼 productive forces
SiliconFlow

SiliconFlow

SiliconFlow is an AI infrastructure that provides developers with LLM deployment, AI model hosting, and inference APIs. It provides users with lower latency, higher throughput and predictable costs through an optimized stack.

Development platform multiple models
💼 productive forces
MagicaL Core

MagicaL Core

MagicaLCore is an application that can perform machine learning work on the iPad. Users can import, organize, train and test machine learning models in real time, and develop and experiment with models directly on the device.

Artificial Intelligence machine learning
💼 productive forces
Labelbox

Labelbox

Labelbox is a data factory designed for AI teams, aiming to provide solutions for building, operating, and data labeling. Its main advantages include flexible annotation tools, automated data processes, rich data management functions, etc. Background information: Labelbox is committed to helping AI teams improve data annotation efficiency and model training quality, and is positioned to provide a comprehensive data management and annotation platform.

Teamwork Model training
💼 productive forces
OpenTrain AI

OpenTrain AI

OpenTrain AI is an AI training data marketplace that lets you directly hire vetted human data experts from around the world, using your favorite annotation software. Reduce costs, maintain control, and quickly build high-quality AI training data.

Artificial Intelligence Data annotation
💼 productive forces
Genie Studio

Genie Studio

Genie Studio is a one-stop development platform specially created by Zhiyuan Robot for embodied intelligence scenarios. It has full-link product capabilities including data collection, model training, simulation evaluation, and model reasoning. It provides developers with standardized solutions from ‘acquisition’ to ‘training’ to ‘testing’ to ‘push’, which greatly lowers the development threshold and improves development efficiency. The platform promotes the rapid development and application of embodied intelligence technology through efficient data collection, flexible model training, accurate simulation evaluation and seamless model reasoning. Genie Studio not only provides powerful tools, but also provides support for the large-scale implementation of embodied intelligence, accelerating the industry's leap to a new stage of standardization, platformization, and mass production.

AI robot
💼 productive forces
Awesome-LLM-Post-training

Awesome-LLM-Post-training

Awesome-LLM-Post-training is a resource library focused on large language model (LLM) post-training methods. It provides an in-depth look at post-LLM training, including tutorials, surveys, and guides. This resource library is based on the paper "LLM Post-Training: A Deep Dive into Reasoning Large Language Models" and aims to help researchers and developers better understand and apply LLM post-training technology. This resource library is free and open and suitable for academic research and industrial applications.

Artificial Intelligence natural language processing
💼 productive forces
ARGO

ARGO

ARGO is a multi-platform AI client designed to provide users with a powerful artificial intelligence assistant with the ability to think independently, task planning and complex task processing. Its main advantage is that it runs locally on the user's device, ensuring data privacy and security. It is suitable for user groups who need to manage and process tasks efficiently and supports multiple operating systems. Permanently open source and free.

Smart chat RAG
💼 productive forces
Firecrawl LLMs.txt generator

Firecrawl LLMs.txt generator

LLMs.txt Generator is an online tool powered by Firecrawl designed to help users generate integrated text files for LLM training and inference from websites. It provides high-quality text data for training large language models by integrating web content, thereby improving model performance and accuracy. The main advantage of this tool is that it is simple and efficient to operate and can quickly generate the required text files. It is mainly aimed at developers and researchers who need large amounts of text data for model training, providing them with a convenient solution.

text generation API
💼 productive forces
MoBA

MoBA

MoBA (Mixture of Block Attention) is an innovative attention mechanism designed for large language models in long text contexts. It enables efficient long sequence processing by dividing context into chunks and letting each query token learn to focus on the most relevant chunks. The main advantage of MoBA is its ability to seamlessly switch between full attention and sparse attention, which not only ensures performance but also improves computational efficiency. This technology is suitable for tasks that require processing long texts, such as document analysis, code generation, etc., and can significantly reduce computing costs while maintaining high performance of the model. The open source implementation of MoBA provides researchers and developers with powerful tools to advance the application of large language models in the field of long text processing.

large language model Transformer
💼 productive forces
OLMoE app

OLMoE app

OLMoE is an open source language model application developed by Ai2 to provide researchers and developers with a completely open toolkit for conducting artificial intelligence experiments on devices. The app supports offline operation on iPhone and iPad, ensuring user data is completely private. It is built on an efficient OLMoE model and is optimized and quantized to maintain high performance when running on mobile devices. The open source nature of the application makes it an important foundation for research and development of a new generation of on-device artificial intelligence applications.

Artificial Intelligence Open source
💼 productive forces
DeepSeek-R1-Distill-Qwen-32B

DeepSeek-R1-Distill-Qwen-32B

DeepSeek-R1-Distill-Qwen-32B is a high-performance language model developed by the DeepSeek team, based on the Qwen-2.5 series for distillation optimization. The model performs well on multiple benchmarks, especially on math, coding, and reasoning tasks. Its main advantages include efficient reasoning capabilities, powerful multi-language support, and open source features, which facilitate secondary development and application by researchers and developers. This model is suitable for scenarios that require high-performance text generation, such as intelligent customer service, content creation, and code assistance, and has broad application prospects.

Open source Multi-language support
💼 productive forces
ai-data-science-team

ai-data-science-team

This product is an AI-driven data science team model designed to help users complete data science tasks faster. It automates and accelerates data science workflows through a series of professional data science agents (Agents), such as data cleaning, feature engineering, modeling, etc. The main advantage of this product is that it can significantly improve the efficiency of data science work and reduce manual intervention. It is suitable for enterprises and research institutions that need to quickly process and analyze large amounts of data. The product is currently in the Beta stage and is under active development, and there may be breakthrough changes. It adopts the MIT license, and users can use and contribute code for free on GitHub.

AI automation
💼 productive forces
Bespoke Labs

Bespoke Labs

Bespoke Labs focuses on providing high-quality customized data set services to support engineers in precise model fine-tuning. The company was co-founded by former Google DeepMind employees Mahesh and UT Austin's Alex to improve access to high-quality data, which is critical to advancing the field. The tools and platforms provided by Bespoke Labs, such as Minicheck, Evalchemy and Curator, are designed around the creation and management of datasets to improve data quality and model performance.

synthetic data Model fine-tuning
💼 productive forces
OpenEMMA

OpenEMMA

OpenEMMA is an open source project that reproduces Waymo's EMMA model and provides an end-to-end framework for motion planning of autonomous vehicles. The model leverages pre-trained visual language models (VLMs) such as GPT-4 and LLaVA to integrate text and forward-looking camera input to achieve accurate predictions of its own future waypoints and provide reasons for decision-making. The goal of OpenEMMA is to provide researchers and developers with easily accessible tools to advance autonomous driving research and applications.

Open source multimodal
💼 productive forces