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Granite 4.0 Nano language models are lightweight, state-of-the-art open foundation models designed for scenarios where efficiency and speed are critical. They can run on resource-constrained devices such as smartphones or IoT hardware, enabling offline and privacy-preserving applications. These models support a wide range of coding tasks—including fill-in-the-middle (FIM) code completion through the use of specialized prefix and suffix tokens, retrieval-augmented generation (RAG), tool usage, and structured JSON output.
All models are publicly released under the Apache 2.0 license, allowing free use for both research and commercial purposes. The data curation and training processes were specifically designed for enterprise scenarios and customization, incorporating governance, risk, and compliance (GRC) evaluations alongside IBM’s standard data clearance and document quality review procedures.
Granite 4.0 Nano models are available in two sizes, 350M and 1B parameters. This model family supports dense and dense-hybrid architectures. We release both base models (checkpoints after pretraining) and instruct models (checkpoints fine-tuned for dialogue, instruction following, helpfulness, and safety).
Core evaluation results for all model variants are provided on their respective model cards, and a more comprehensive extended evaluation is available here.
To use any of our models, pick an appropriate model_path from:
ibm-granite/granite-4.0-1b-baseibm-granite/granite-4.0-1bibm-granite/granite-4.0-h-1b-baseibm-granite/granite-4.0-h-1bibm-granite/granite-4.0-350m-baseibm-granite/granite-4.0-350mibm-granite/granite-4.0-h-350m-baseibm-granite/granite-4.0-h-350m
This is a simple example of how to use Granite-4.0-350M model.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "auto"
model_path = "ibm-granite/granite-4.0-350m"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
chat = [
{ "role": "user", "content": "What is the name of the durable rock known for being one of the hardest natural building stones?"},
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens,
max_new_tokens=150)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output)Agentic tool-calling is shaping the future of AI agents, enabling seamless integration of powerful back-end systems into agent-driven workflows. These trajectories often involve multiple tool calls, handling execution responses, and multi-turn user interactions. While agent frameworks orchestrate long-horizon tasks, LLMs must provide the foundation — including standard tool formats, robust tool-call handling (even in edge cases), and support for feeding back execution results.
The following code example demonstrates how Granite 4.0 Nano models tool-calling capabilities address these needs. In the first user query, the model successfully generates the appropriate tool call because it has access to the necessary tools. In contrast, it produces an apology message for the second query, as the required tooling is unavailable. Since this example does not use an agent framework, tool execution is simulated.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
# model_path = ""
model_path = "ibm-granite/granite-4.0-350m"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
chat=[
{"role": "user", "content": "I'm looking to buy a used truck for my construction work, but I want to make sure it's legitimate. The seller provided the VIN: 1FMXK92W8YPA12345 and said it's registered in Georgia. Can you verify if the VIN is valid and matches a registered vehicle?"},
{"role": "assistant",
"content": "",
"tool_calls": [
{
"function": {
"name": "check_valid_vin",
"arguments": {"vin": "1FMXK92W8YPA12345"}
}
}
]
},
{"role": "tool", "content": "{\"valid\": true, \"vin_details\": {\"make\": \"Ford\", \"model\": \"F-150\", \"year\": 2020, \"vehicle_type\": \"Truck\", \"registration_status\": \"Active\", \"registration_state\": \"GA\", \"odometer\": 82345, \"title_status\": \"Clear\", \"lienholder\": null, \"recall_history\": \"No active recalls\"}, \"notes\": \"VIN is valid and registered in Georgia. PPSR lien check complete - no security interests found. License plate verification requires separate DMV lookup which is not currently available through this tool.\"}"},
{"role": "user", "content": "I'm also considering purchasing a new Ford F-150 from an official dealership in Texas. Could you provide a cost estimate for this type of truck in that state?"},
]
tools = [
{
"type": "function",
"function": {
"name": "check_valid_registration",
"description": "Verifies whether a vehicle registration number is valid for a specific state and returns detailed information about the registered vehicle if valid. Use this function to validate vehicle registration status and obtain ownership/vehicle data.",
"parameters": {
"type": "object",
"properties": {
"reg": {
"type": "string",
"description": "Vehicle registration number in standard format (e.g., ABC123 or XYZ-7890)"
},
"state": {
"type": "string",
"description": "Two-letter state abbreviation where the vehicle is registered (e.g., CA for California, NSW for New South Wales, or TX for Texas)"
}
},
"required": ["reg", "state"],
}
}
},
{
"type": "function",
"function": {
"name": "check_valid_vin",
"description": "Verifies if a vehicle identification number (VIN) corresponds to a registered vehicle in official records. Returns comprehensive vehicle details including make, model, year, registration status, and ownership information if valid.",
"parameters": {
"type": "object",
"properties": {
"vin": {
"type": "string",
"description": "The 17-character Vehicle Identification Number to validate. Must follow standard VIN format (uppercase alphanumeric characters, no spaces or special characters). Case-insensitive validation performed internally."
}
},
"required": ["vin"],
}
}
},
{
"type": "function",
"function": {
"name": "ppsr_lookup_by_vin",
"description": "Performs a PPSR (Personal Property Securities Register) lookup for a vehicle using its VIN. Returns search results including security interests, ownership status, and an official PDF certificate URL. Use this to verify vehicle history or security claims.",
"parameters": {
"type": "object",
"properties": {
"vin": {
"type": "string",
"description": "17-character alphanumeric vehicle identification number (ISO 3779 standard). Case-insensitive. Example: '1HGCM82633A123456'"
}
},
"required": ["vin"]
}
}
},
]
chat = tokenizer.apply_chat_template(chat,tokenize=False, add_generation_prompt=True, tools=tools)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens,
max_new_tokens=1000)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output[0])Granite natively supports structured JSON output, a capability that proves useful in many real-world scenarios. Below, we demonstrate how it can be applied to parse natural language IT support tickets.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model_path = "ibm-granite/granite-4.0-350m"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
chat = [
{
"role": "system",
"content": "You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{\"title\":\"ITSupportTicket\",\"type\":\"object\",\"properties\":{\"ticketID\":{\"type\":\"string\"},\"requester\":{\"type\":\"object\",\"properties\":{\"name\":{\"type\":\"string\"},\"email\":{\"type\":\"string\",\"format\":\"email\"}},\"required\":[\"name\",\"email\"]},\"category\":{\"type\":\"string\",\"enum\":[\"Access\",\"Hardware\",\"Software\",\"Network\",\"Other\"]},\"priority\":{\"type\":\"string\",\"enum\":[\"Low\",\"Medium\",\"High\",\"Critical\"]},\"description\":{\"type\":\"string\"},\"reportedAt\":{\"type\":\"string\",\"format\":\"date-time\"}},\"required\":[\"ticketID\",\"requester\",\"category\",\"priority\",\"description\",\"reportedAt\"]}\n</schema>\n"
},
{
"role": "user",
"content": "Please breakdown the follwing IT ticket's content and classify it for me.\n# This is the ticket:\nI can't access the VPN from home—keeps timing out after authentication. Please mark this as High priority. I'm Jordan Lee, [email protected]. Create the ticket as TCK-10944. I noticed the issue today around 2025-10-01T08:35:00Z."
}
]
chat = tokenizer.apply_chat_template(chat,tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens,
max_new_tokens=200)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output[0])The FIM (Fill-in-the-Middle) code completion capability is highly valuable for software developers, as it enables models to intelligently generate missing code segments within existing functions. The example below demonstrates how it can be used to complete a function that processes user data and produces a summary.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
# model_path = ""
model_path = "ibm-granite/granite-4.0-350m"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
prompt = """<|fim_prefix|>
def summarize_users(users):
\"\"\"
Given a list of user dictionaries with 'name' and 'age',
return a summary with the average age and a list of names.
\"\"\"
summary = {}
<|fim_suffix|>
return summary
<|fim_middle|>
"""
chat = [
{ "role": "user", "content": prompt},
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens,
max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output[0])The model of choice (ibm-granite/granite-4.0-350m in this example) can be cloned using:
git clone https://https://huggingface.co/ibm-granite/ibm-granite/granite-4.0-350mPlese check our Guidelines and Code of Conduct to contribute to our project.
The model cards for each model variant are available in their respective HuggingFace repository. Please visit our collection here.
All Granite 4.0 Nano Language Models are distributed under Apache 2.0 license.
Please let us know your comments about our family of language models by visiting our collection. Select the repository of the model you would like to provide feedback about. Then, go to Community tab, and click on New discussion. Alternatively, you can also post any questions/comments on our github discussions page.
If you find granite models useful, please cite our work as follows:
@misc{granite2025,
author = {{IBM Research}},
title = {Granite 4.0 Nano Language Models},
year = {2025},
howpublished = {\url{https://github.com/ibm-granite/granite-4.0-nano-language-models}},
note = {Accessed: 2025-10-23}
}