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ibm-granite_granite-4.1-8B-BF16.gguf

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Granite-4.1-8B

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 Granite-4.1-8B model.

Install the following libraries:

shell

pip install torch torchvision torchaudio
pip install accelerate
pip install transformers

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

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"
model_path = "ibm-granite/granite-4.1-8b"
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": "Please list one IBM Research laboratory located in the United States. You should only output its name and location." },
]
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])

Expected output:

shell

<|start_of_role|>system<|end_of_role|>You are a helpful assistant. Please ensure responses are professional, accurate, and safe.<|end_of_text|>
<|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|>Almaden Research Center, San Jose, California<|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:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"
model_path = "ibm-granite/granite-4.1-8b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()

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
chat = [
    { "role": "user", "content": "What's the weather like in Boston right now?" },
]
chat = tokenizer.apply_chat_template(chat, \
                                     tokenize=False, \
                                     tools=tools, \
                                     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])

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:

Benchmarks	Metric	3B Dense	8B Dense	30B Dense
General Tasks
MMLU	5-shot	67.02	73.84	80.16
MMLU-Pro	5-shot, CoT	49.83	55.99	64.09
BBH	3-shot, CoT	75.83	80.51	83.74
AGI EVAL	0-shot, CoT	65.16	72.43	77.80
GPQA	0-shot, CoT	31.70	41.96	45.76
SimpleQA		3.68	4.82	6.81
Alignment Tasks
AlpacaEval 2.0		38.57	50.08	56.16
IFEval Avg		82.30	85.87	88.88
ArenaHard		37.80	68.98	71.02
MTBench Avg		7.57	8.61	8.61
Math Tasks
GSM8K	8-shot	86.88	92.49	94.16
GSM Symbolic	8-shot	81.32	83.70	75.70
Minerva Math	0-shot, CoT	67.94	80.10	81.32
DeepMind Math	0-shot, CoT	64.64	80.07	81.93
Code Tasks
HumanEval	pass@1	81.71	85.37	88.41
HumanEval+	pass@1	76.83	79.88	85.37
MBPP	pass@1	71.16	87.30	85.45
MBPP+	pass@1	62.17	73.81	73.54
CRUXEval-O	pass@1	40.75	47.63	55.75
BigCodeBench	pass@1	32.19	35.00	38.77
MULTIPLE	pass@1	52.54	60.26	62.31
Eval+ Avg	pass@1	67.05	80.21	82.66
Tool Calling Tasks
BFCL v3		60.80	68.27	73.68
Multilingual Tasks
MMMLU	5-shot	57.61	64.84	73.71
INCLUDE	5-shot	52.05	58.89	67.26
MGSM	8-shot	70.00	82.32	71.12
Safety
SALAD-Bench		93.95	95.80	96.41
AttaQ		81.88	81.19	85.76
Tulu3 Safety Eval Avg		66.84	75.57	78.19

**Multilingual Benchmarks and the included languages:**
Benchmarks	# Langs	Languages
MMMLU	11	ar, de, en, es, fr, ja, ko, pt, zh, bn, hi
INCLUDE	14	hi, bn, ta, te, ar, de, es, fr, it, ja, ko, nl, pt, zh
MGSM	5	en, es, fr, ja, zh

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.

Model	3B Dense	8B Dense	30B Dense
Embedding size	2560	4096	4096
Number of layers	40	40	64
Attention head size	64	128	128
Number of attention heads	40	32	32
Number of KV heads	8	8	8
MLP / Shared expert hidden size	8192	12800	32768
MLP activation	SwiGLU	SwiGLU	SwiGLU
Sequence length	4096	4096	4096
Position embedding	RoPE	RoPE	RoPE
# Parameters	3B	8B	30B

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-Tunning 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 rigorus 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 case, 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. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks.

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

n810x/ibm-granite_granite-4.1-8B-BF16.gguf

Granite-4.1-8B

n810x/
ibm-granite_granite-4.1-8B-BF16.gguf