Mamba-Chat

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Mamba-Chat is an open-source chat language model based on a state-space model architecture, not a transformer. It provides training and fine-tuning code for developers to build custom chat applications.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is mamba-chat?

Mamba-Chat is a groundbreaking chat language model that leverages a state-space model architecture, distinguishing it from traditional transformer-based LLMs. Developed by redotvideo, it is based on Albert Gu's and Tri Dao's Mamba work and offers training and fine-tuning capabilities. The model, specifically Mamba-2.8B, has been fine-tuned on 16,000 samples from the HuggingFaceH4/ultrachat_200k dataset. This open-source project provides developers with the necessary code to run a CLI chatbot, a Gradio app, and fine-tune the base Mamba model, making it an excellent resource for experimenting with novel LLM architectures.

Best used for

Ideal for developers and data scientists who need to build custom chat applications, fine-tune large language models, and experiment with novel AI architectures. Especially valuable for those interested in state-space models as an alternative to transformers.

Common actions

build chatbots

fine-tune LLMs

experiment with AI models

develop conversational AI

collaborationworkflowsautomated workflowopen-sourcelow-code/no-codedeepfakegithub copilot"AI Agents"face swapping

Capabilities

Key features

State-space model architecture
Training code provided
Fine-tuning code provided
CLI chatbot
Gradio app

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the core difference between Mamba-Chat and other LLMs?

Mamba-Chat is unique because it is based on a state-space model architecture, specifically Mamba, rather than the transformer architecture commonly used in most large language models. This offers a different approach to sequence modeling.

Can I fine-tune Mamba-Chat with my own data?

Yes, the repository provides training and fine-tuning code. You can use your own datasets to fine-tune the Mamba base model, similar to how Mamba-Chat was fine-tuned on the Ultrachat dataset.

What are the hardware requirements for running or fine-tuning Mamba-Chat?

Running the chat application can be done on standard setups. For fine-tuning, especially with larger models, a GPU with at least 24GB of VRAM (e.g., 3090, 4090) is recommended for optimal performance, though settings can be adjusted for less powerful cards.

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Coding & Development › Open Source & Models Research & Education › Scientific Computing

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