Model Summary: Granite-3.0-2B-Base is a decoder-only language model to support a variety of text-to-text generation tasks. It is trained from scratch following a two-stage training strategy. In the first stage, it is trained on 10 trillion tokens sourced from diverse domains. During the second stage, it is further trained on 2 trillion tokens using a carefully curated mix of high-quality data, aiming to enhance its performance on specific tasks.
Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 models for languages beyond these 12 languages.
Intended use: Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and more. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.
Generation: This is a simple example of how to use Granite-3.0-2B-Base model.
Then, copy the code snippet below to run the example.
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "auto"
model_path = "ibm-granite/granite-3.0-2b-base"
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
input_text = "Where is the Thomas J. Watson Research Center located?"
# tokenize the text
input_tokens = tokenizer(input_text, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens,
max_length=4000)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output)
Model Architecture: Granite-3.0-2B-Base is based on a decoder-only dense transformer architecture. Core components of this architecture are: GQA and RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
Model
2B Dense
8B Dense
1B MoE
3B MoE
Embedding size
2048
4096
1024
1536
Number of layers
40
40
24
32
Attention head size
64
128
64
64
Number of attention heads
32
32
16
24
Number of KV heads
8
8
8
8
MLP hidden size
8192
12800
512
512
MLP activation
SwiGLU
SwiGLU
SwiGLU
SwiGLU
Number of Experts
—
—
32
40
MoE TopK
—
—
8
8
Initialization std
0.1
0.1
0.1
0.1
Sequence Length
4096
4096
4096
4096
Position Embedding
RoPE
RoPE
RoPE
RoPE
# Parameters
2.5B
8.1B
1.3B
3.3B
# Active Parameters
2.5B
8.1B
400M
800M
# Training tokens
12T
12T
10T
10T
Training Data: This model is trained on a mix of open source and proprietary data following a two-stage training strategy.
Stage 1 data: The data for stage 1 is sourced from diverse domains, such as: web, code, academic sources, books, and math data.
Stage 2 data: The data for stage 2 comprises a curated mix of high-quality data from the same domains, plus multilingual and instruction data. The goal of this second training phase is to enhance the model’s performance on specific tasks.
Infrastructure: We train Granite 3.0 Language Models using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs while minimizing environmental impact by utilizing 100% renewable energy sources.
Ethical Considerations and Limitations: The use of Large Language Models involves risks and ethical considerations people must be aware of, including but not limited to: bias and fairness, misinformation, and autonomous decision-making. Granite-3.0-2B-Base model is not the exception in this regard. Even though this model is suited for multiple generative AI tasks, it has not undergone any safety alignment, there it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying text verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use Granite-3.0-2B-Base model with ethical intentions and in a responsible way.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("ibm-granite/granite-3.0-2b-base", revision="a8462c21f5e1f27be4536dc36d6cc789da23fbd6")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-3.0-2b-base", revision="a8462c21f5e1f27be4536dc36d6cc789da23fbd6", device_map="auto")
