Model Summary:
Granite-3.1-1B-A400M-Base extends the context length of Granite-3.0-1B-A400M-Base from 4K to 128K using a progressive training strategy by increasing the supported context length in increments while adjusting RoPE theta until the model has successfully adapted to desired length of 128K. This long-context pre-training stage was performed using approximately 500B tokens.
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:
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-3.1-1B-A400M-Base model.
Then, copy the code snippet below to run the example.
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
2device ="auto"3model_path ="ibm-granite/granite-3.1-1b-a400m-base"4tokenizer = AutoTokenizer.from_pretrained(model_path)5# drop device_map if running on CPU6model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)7model.eval()8# change input text as desired9input_text ="Where is the Thomas J. Watson Research Center located?"10# tokenize the text11input_tokens = tokenizer(input_text, return_tensors="pt").to(device)12# generate output tokens13output = model.generate(**input_tokens,14 max_length=4000)15# decode output tokens into text16output = tokenizer.batch_decode(output)17# print output18print(output)
Evaluation Results:
HuggingFace Open LLM Leaderboard V1
Models
ARC-Challenge
Hellaswag
MMLU
TruthfulQA
Winogrande
GSM8K
Avg
Granite-3.1-8B-Base
63.99
83.27
63.45
51.29
78.92
60.19
66.85
Granite-3.1-2B-Base
53.58
77.67
52.86
39.02
72.84
47.99
57.32
Granite-3.1-3B-A800M-Base
50.76
74.45
48.31
39.91
69.29
40.56
53.88
Granite-3.1-1B-A400M-Base
39.42
66.13
26.53
37.67
2.03
18.87
31.78
HuggingFace Open LLM Leaderboard V2
Models
IFEval
BBH
MATH Lvl 5
GPQA
MUSR
MMLU-Pro
Avg
Granite-3.1-8B-Base
42.21
26.02
9.52
9.51
8.36
24.8
20.07
Granite-3.1-2B-Base
35.22
16.84
5.59
3.69
3.9
13.9
13.19
Granite-3.1-3B-A800M-Base
29.96
11.91
4
3.69
1.11
8.81
9.91
Granite-3.1-1B-A400M-Base
25.19
6.43
2.19
0.22
1.76
1.55
6.22
Model Architecture:
Granite-3.1-1B-A400M-Base is based on a decoder-only sparse Mixture of Experts (MoE) transformer architecture. Core components of this architecture are: Fine-grained Experts, Dropless Token Routing, and Load Balancing Loss.
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
128K
128K
128K
128K
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.
Stage 3 data: The data for stage 3 consists of original stage-2 pretraining data with additional synthetic long-context data in form of QA/summary pairs where the answer contains a recitation of the related paragraph before the answer.
Infrastructure:
We train Granite 3.1 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.
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-3.1-1B-A400M-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-3.1-1B-A400M-Base model with ethical intentions and in a responsible way.
