Granite 3.3 8b Instruct Alternatives

Granite 3.1 8B Instruct is IBM's open-weight language model optimized for instruction following, tool use, and grounded responses. It includes specialized system prompts for knowledge cutoffs, tool calling, and document-based answering. The model is available via Hugging Face and supports integration with Diffusers-style pipelines and inference providers.

Granite 4.1 8b is an open-source foundation model hosted on Hugging Face under the repository ibm-granite/granite-4.1-8b. It functions as a language model that processes text and supports structured interactions including tool use and document referencing. The model includes a defined tokenizer with specific tokens such as a beginning-of-text marker, end-of-text marker, pad token, and unknown token. Its chat template is implemented in Jinja format and provides system messages for handling tools via XML-tagged function calls that return JSON objects containing a function name and arguments. A separate document-oriented system message prefix is also defined for scenarios involving provided documents. These elements enable the model to manage assistant-like interactions that incorporate external tools or reference materials when responding to queries. It is delivered as a downloadable model on the Hugging Face platform, where users can access it alongside other models, datasets, and related resources. The surrounding platform supports community features, documentation, and inference options but the model entry itself centers on the architecture and template definitions for text-based tasks.

Granite-4.1-3B is a compact open-source language model developed by IBM. It features strong performance on tool use, function calling, and agentic workflows. The model includes a specialized chat template for structured tool interactions and can be run locally via Hugging Face Transformers or optimized inference engines. It is part of the Granite family of models focused on enterprise and developer use cases.

granite-speech-3.3-2b is part of IBM's Granite family, a 2B parameter model supporting speech and text modalities. It includes built-in tool calling, system prompts, and is optimized for helpful assistant behavior. Available on Hugging Face, it is aimed at developers creating voice-enabled applications or multimodal AI agents that require efficient on-device or cloud inference.

Granite 4.1 3b is a 3-billion-parameter language model released by IBM in GGUF format on Hugging Face. The repository provides the quantized model files along with system prompt templates that define its behavior for specific tasks. The included templates configure the model to support tool calling through a defined XML-based format. When tools are supplied, the model receives instructions to return function calls inside designated XML tags containing JSON objects with a function name and arguments. If a requested tool is absent from the provided list, the model is directed to inform the user it cannot fulfill the request. A separate template addresses document-grounded responses. It instructs the model that it has access to a list of documents enclosed in XML tags and must align all answers strictly with facts from those documents. When the required information is unavailable, the model must state that the question cannot be answered based on the available data. The model is delivered as GGUF files suitable for local inference. The repository forms part of IBM's open Granite model family and is hosted on the Hugging Face platform, which supports open-source and open-science initiatives. No pricing, licensing terms, or additional deployment details are specified in the repository page.

granite-4.0-micro is a compact language model developed by IBM as part of the Granite 4.0 series. It is optimized for instruction following and tool/function calling tasks. The model is designed for efficient deployment in enterprise environments and is fully open source on Hugging Face.

ibm-granite/granite-speech-4.1-2b is a 2-billion-parameter automatic speech recognition model from IBM's Granite family. Built for efficient transcription, it integrates with the Hugging Face Transformers library and supports standard chat and inference pipelines. The model is suitable for on-premise or cloud deployment where accurate speech-to-text is required.

Granite Vision 3.3 2B is an open-weight vision-language model developed by IBM. It supports image and document understanding tasks and can be run locally or through cloud inference providers.

IBM Granite Speech 4.1 2B Plus is an open-source speech foundation model supporting both automatic speech recognition and text-to-speech capabilities. The 2-billion-parameter model includes advanced features such as tool calling within conversations and is optimized for local deployment. It is hosted on Hugging Face and installable via pip for integration into voice-enabled applications and research projects.

ibm-granite/granite-speech-4.1-2b-nar is a 2B parameter speech model that supports tool calling and document-based assistance. It features a sophisticated chat template for handling function calls and document references. The model is designed for integration into voice-enabled AI applications and agents.

This is a 1B parameter speech model from IBM's Granite 4.0 series, designed for automatic speech recognition. It is distributed on Hugging Face and compatible with the Transformers library. The compact size makes it suitable for various deployment scenarios where efficient speech-to-text capabilities are needed.

Granite Vision 4.1 4B is IBM's open multimodal model capable of processing both text and images. It excels at chart-to-code, chart-to-CSV, chart summarization, and table extraction tasks. The model uses a chat template optimized for visual reasoning and is available for local inference through the Hugging Face ecosystem.

Granite-4.0-tiny-preview is a compact preview release of IBM's Granite 4.0 series. It is designed for efficiency while maintaining strong performance on reasoning, tool use, and document-grounded generation. The model includes a custom system prompt template and supports tool calling, making it suitable for integration into business applications and agentic workflows.

Granite-4.0 WebGPU is a Hugging Face Space that allows users to run IBM's Granite-4.0 language model entirely in the browser using WebGPU acceleration. Users type prompts and receive generated text responses with no data leaving their device. It demonstrates efficient on-device inference for privacy-focused AI usage.

1 2B by IBM is a speech-language model designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST). It supports English, French, German, Spanish, Portuguese, and Japanese, with additional translation capabilities for English to Italian and English to Mandarin. The model is intended for enterprise applications that require processing of speech inputs, particularly those involving transcription and translation across multiple languages. This model was trained on 174,000 hours of audio from public corpora and synthetic datasets, including resources tailored for Japanese ASR, keyword-biased ASR, and speech translation tasks. 1 2B introduces a dual-head CTC encoder that outputs both graphemic and byte pair encoding (BPE) representations, enhancing transcription accuracy for multilingual ASR. It incorporates frame importance sampling to focus on informative segments of audio and supports prompt-based punctuation and truecasing for ASR and AST in all supported languages, including German noun capitalization. The model also offers improved keyword list biasing to better recognize names, acronyms, and technical jargon. Performance evaluations were conducted on public benchmarks, with particular focus on English ASR as well as multilingual ASR and AST tasks. Metrics such as punctuation error rate (PER) and capitalization F1 (Cap-F1) were reported across various datasets, demonstrating high accuracy in punctuation and capitalization, especially for German. The model’s keyword list biasing capability was evaluated using F1 scores for transcribed keywords during ASR tasks. 0 license. 1 or higher) and is compatible with vLLM. The model weights are accessible through GitHub, and usage examples are provided for both online and offline inference. The tool is positioned within the class of multilingual speech recognition and translation models, tailored for enterprise deployment.