Granite-3.1-2b-instruct-Q4_K_M.gguf

Model Summary: Granite-3.1-2B-Instruct is a 2B parameter long-context instruct model finetuned from Granite-3.1-2B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.

Developers: Granite Team, IBM
GitHub Repository: ibm-granite/granite-3.1-language-models
Website: Granite Docs
Cutoff Date: April 2024
Release Date: December 18th, 2024
License: Apache 2.0

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages.

Intended Use: The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.

Capabilities

Summarization
Text classification
Text extraction
Question-answering
Retrieval Augmented Generation (RAG)
Code related tasks
Function-calling tasks
Multilingual dialog use cases
Long-context tasks including long document/meeting summarization, long document QA, etc.

Generation: This is a simple example of how to use Granite-3.1-2B-Instruct model.

Install the following libraries:

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.

import torch from transformers import AutoModelForCausalLM, AutoTokenizer

device = "auto" model_path = "ibm-granite/granite-3.1-2b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_path)

model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device) model.eval()

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)

input_tokens = tokenizer(chat, return_tensors="pt").to(device)

output = model.generate(**input_tokens, max_new_tokens=100)

output = tokenizer.batch_decode(output)

print(output)