Instructions to use ruslanmv/granite-3.1-8b-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ruslanmv/granite-3.1-8b-Reasoning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ruslanmv/granite-3.1-8b-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ruslanmv/granite-3.1-8b-Reasoning")
model = AutoModelForCausalLM.from_pretrained("ruslanmv/granite-3.1-8b-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ruslanmv/granite-3.1-8b-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ruslanmv/granite-3.1-8b-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ruslanmv/granite-3.1-8b-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ruslanmv/granite-3.1-8b-Reasoning

SGLang

How to use ruslanmv/granite-3.1-8b-Reasoning with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ruslanmv/granite-3.1-8b-Reasoning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ruslanmv/granite-3.1-8b-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ruslanmv/granite-3.1-8b-Reasoning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ruslanmv/granite-3.1-8b-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ruslanmv/granite-3.1-8b-Reasoning with Docker Model Runner:
```
docker model run hf.co/ruslanmv/granite-3.1-8b-Reasoning
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Granite-3.1-8B-Reasoning (Fine-Tuned for Advanced Reasoning)

Model Overview

This model is a fine-tuned version of ibm-granite/granite-3.1-8b-instruct, optimized for logical reasoning and analytical tasks. Fine-tuning has been performed to enhance structured problem-solving, long-context comprehension, and instruction-following capabilities.

Developed by: ruslanmv
License: Apache 2.0
Base Model: ibm-granite/granite-3.1-8b-instruct
Fine-tuned for: Logical reasoning, structured problem-solving, and long-context tasks
Training Framework: Unsloth & Hugging Face TRL (2x faster training)
Supported Languages: English
Model Size: 8.17B params
Tensor Type: BF16

Why Use This Model?

This fine-tuned model improves upon the base Granite-3.1-8B model by enhancing its reasoning capabilities while retaining its general text-generation abilities.

✅ Optimized for complex reasoning tasks
✅ Enhanced long-context understanding
✅ Improved instruction-following abilities
✅ Fine-tuned for structured analytical thinking

Installation & Usage

Install the required dependencies:

pip install torch torchvision torchaudio
pip install accelerate
pip install transformers

Running the Model

Use the following Python snippet to load and generate text with Granite-3.1-8B-Reasoning:

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch

# Model and tokenizer
model_name = "ruslanmv/granite-3.1-8b-Reasoning" # Or "ruslanmv/granite-3.1-2b-Reasoning"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map='auto', # or 'cuda' if you have only one GPU
    torch_dtype=torch.float16, # Use float16 for faster and less memory intensive inference
    load_in_4bit=True # Enable 4-bit quantization for lower memory usage - requires bitsandbytes
)

# Prepare dataset
SYSTEM_PROMPT = """
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""
text = tokenizer.apply_chat_template([
    {"role" : "system", "content" : SYSTEM_PROMPT},
    {"role" : "user", "content" : "Calculate pi."},
], tokenize = False, add_generation_prompt = True)

inputs = tokenizer(text, return_tensors="pt").to("cuda") # Move input tensor to GPU

# Sampling parameters
generation_config = GenerationConfig(
    temperature = 0.8,
    top_p = 0.95,
    max_new_tokens = 1024, # Equivalent to max_tokens in the original code, but for generation
)

# Inference
with torch.inference_mode(): # Use inference mode for faster generation
    outputs = model.generate(**inputs, generation_config=generation_config)

output = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Find the start of the actual response
start_index = output.find("assistant")
if start_index != -1:
    # Remove the initial part including "assistant"
    output = output[start_index + len("assistant"):].strip()

print(output)

You will get something like:

<reasoning>
Pi is an irrational number, which means it cannot be exactly calculated as it has an infinite number of decimal places. However, we can approximate pi using various mathematical formulas. One of the simplest methods is the Leibniz formula for pi, which is an infinite series:

pi = 4 * (1 - 1/3 + 1/5 - 1/7 + 1/9 - 1/11 +...)

This series converges to pi as more terms are added.
</reasoning>

<answer>
The exact value of pi cannot be calculated due to its infinite decimal places. However, using the Leibniz formula, we can approximate pi to a certain number of decimal places. For example, after calculating the first 500 terms of the series, we get an approximation of pi as 3.1415926535897932384626433832795028841971693993751058209749445923078164062862089986280348253421170679.
</answer>

Intended Use

Granite-3.1-8B-Reasoning is designed for tasks requiring structured and logical reasoning, including:

Logical and analytical problem-solving
Text-based reasoning tasks
Mathematical and symbolic reasoning
Advanced instruction-following
Conversational AI with a focus on structured responses

This model is particularly useful for enterprise AI applications, research, and large-scale NLP tasks.

License & Acknowledgments

This model is released under the Apache 2.0 license. It is fine-tuned from IBM’s Granite 3.1-8B-Instruct model. Special thanks to the IBM Granite Team for developing the base model.

For more details, visit the IBM Granite Documentation.

Citation

If you use this model in your research or applications, please cite:

@misc{ruslanmv2025granite,
  title={Fine-Tuning Granite-3.1-8B for Advanced Reasoning},
  author={Ruslan M.V.},
  year={2025},
  url={https://huggingface.co/ruslanmv/granite-3.1-8b-Reasoning}
}

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for ruslanmv/granite-3.1-8b-Reasoning

Base model

ibm-granite/granite-3.1-8b-base

Finetuned

ibm-granite/granite-3.1-8b-instruct

Finetuned

(15)

this model

Quantizations

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granite-3.1-8b-Reasoning