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LemOneLabs/
Granite-4.1-8b-Onnx

Name: Granite-4.1-8b-Onnx
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ibm-granite/granite-4.1-8b

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apache-2.0

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Granite-4.1-8B-ONNX

Model Summary: Granite-4.1-8B is a 8B parameter long-context instruct model finetuned from Granite-4.1-8B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. Granite 4.1 models have gone through an improved post-training pipeline, including supervised finetuning and reinforcement learning alignment, resulting in enhanced tool calling, instruction following, and chat capabilities.

Developers: Granite Team, IBM
HF Collection: Granite 4.1 Language Models HF Collection
Technical Blog: Granite-4.1 Blog
GitHub Repository: ibm-granite/granite-4.1-language-models
Website: Granite Docs
Release Date: April 29th, 2026
License: Apache 2.0

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages.

Intended use: The model is designed to follow general instructions and can serve as the foundation for AI assistants across diverse domains, including business applications, as well as for LLM agents equipped with tool-use capabilities.

Capabilities

Summarization
Text classification
Text extraction
Question-answering
Retrieval Augmented Generation (RAG)
Code related tasks
Function-calling tasks
Multilingual dialog use cases
Fill-In-the-Middle (FIM) code completions

Generation: This is a simple example of how to use the Granite-4.1-8B model directly in your JavaScript/TypeScript environments (like Node.js, Deno, Bun, or the browser) using Transformers.js.

Install the library:

shell

npm install @huggingface/transformers

Then, copy the snippet from the section that is relevant for your use case.

javascript

import { AutoModelForCausalLM, AutoTokenizer } from '@huggingface/transformers';

const model_id = "LemOneLabs/Granite-4.1-8b-Onnx";
const tokenizer = await AutoTokenizer.from_pretrained(model_id);

// Load model. Uses WASM by default. For hardware acceleration, specify { device: 'webgpu' }
const model = await AutoModelForCausalLM.from_pretrained(model_id, {
    device: 'webgpu' // Drop this options object if WebGPU is not supported in your environment
});

// change input text as desired
const chat = [
    { role: "user", content: "Please list one IBM Research laboratory located in the United States. You should only output its name and location." }
];

const input_text = tokenizer.apply_chat_template(chat, { 
    tokenize: false, 
    add_generation_prompt: true 
});

// tokenize the text
const input_tokens = tokenizer(input_text);

// generate output tokens
const output = await model.generate({
    ...input_tokens,
    max_new_tokens: 100
});

// decode output tokens into text
const decoded = tokenizer.batch_decode(output, { skip_special_tokens: false });

// print output
console.log(decoded[0]);

Expected output:

shell

<|start_of_role|>user<|end_of_role|>Please list one IBM Research laboratory located in the United States. You should only output its name and location.<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>IBM Almaden Research Laboratory, San Jose, California, United States.<|end_of_text|>

Tool-calling: Granite-4.1-8B comes with enhanced tool calling capabilities, enabling seamless integration with external functions and APIs. To define a list of tools please follow OpenAI's function definition schema.

This is an example of how to use Granite-4.1-8B model tool-calling ability with Transformers.js:

javascript

import { AutoModelForCausalLM, AutoTokenizer } from '@huggingface/transformers';

const model_id = "ibm-granite/granite-4.1-8b";
const tokenizer = await AutoTokenizer.from_pretrained(model_id);

// Load model. Uses WASM by default. For hardware acceleration, specify { device: 'webgpu' }
const model = await AutoModelForCausalLM.from_pretrained(model_id, {
    device: 'webgpu' // Drop this options object if WebGPU is not supported in your environment
});

const tools = [
    {
        type: "function",
        function: {
            name: "get_current_weather",
            description: "Get the current weather for a specified city.",
            parameters: {
                type: "object",
                properties: {
                    city: {
                        type: "string",
                        description: "Name of the city"
                    }
                },
                required: ["city"]
            }
        }
    }
];

// change input text as desired
const chat = [
    { role: "user", content: "What's the weather like in Boston right now?" },
];

const input_text = tokenizer.apply_chat_template(chat, { 
    tokenize: false,
    add_generation_prompt: true,
    tools: tools
});

// tokenize the text
const input_tokens = tokenizer(input_text);

// generate output tokens
const output = await model.generate({
    ...input_tokens,
    max_new_tokens: 100
});

// decode output tokens into text
const decoded = tokenizer.batch_decode(output, { skip_special_tokens: false });

// print output
console.log(decoded[0]);

Expected output:

shell

<|start_of_role|>system<|end_of_role|>You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.
You are provided with function signatures within <tools></tools> XML tags:
<tools>
{"type": "function", "function": {"name": "get_current_weather", "description": "Get the current weather for a specified city.", "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "Name of the city"}}, "required": ["city"]}}}
</tools>
For each tool call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.<|end_of_text|>
<|start_of_role|>user<|end_of_role|>What's the weather like in Boston right now?<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|><tool_call>
{"name": "get_current_weather", "arguments": {"city": "Boston"}}
</tool_call><|end_of_text|>

Evaluation Results:

Model Architecture:

Granite-4.1-8B baseline is built on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.

Training Data: Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities, and (3) a select set of human-curated data.

Supervised Fine-Tuning and Reinforcement Learning: Instruct model has been fine tuned with significantly improved SFT-pipeline and Reinforcement learning pipelines with high quality mix of various datasets as mentioned above. With rigorous SFT-RL cycles we have improved Granite-4.1 model's tool calling, instruction following and chat capabilities. For further details please check our Granite-4.1 Blog.

Infrastructure: We trained the Granite 4.1 Language Models utilizing an NVIDIA GB200 NVL72 cluster hosted in CoreWeave. Intra-rack communication occurs via the 72-GPU NVLink domain, and a non-blocking, full Fat-Tree NDR 400 Gb/s InfiniBand network provides inter-rack communication. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.

Ethical Considerations and Limitations: Granite 4.1 Instruction Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering multiple languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such cases, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. We urge the community to use this model with proper safety testing and tuning tailored for their specific tasks. To enhance safety in enterprise deployments, we recommend using Granite 4.1 Language models alongside Granite Guardian, a model designed to detect and flag risks in inputs and outputs across key dimensions outlined in the IBM AI Risk Atlas.

Resources

⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources