File size: 10,583 Bytes
464a031
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3f8e541
464a031
 
 
 
 
 
 
 
 
 
3f8e541
 
 
 
 
 
 
 
 
 
8bafb50
3f8e541
 
 
 
 
 
 
 
 
464a031
 
 
 
 
 
 
 
 
3f8e541
464a031
 
 
 
 
 
 
 
 
3f8e541
464a031
 
 
 
 
 
588f56e
 
 
 
 
 
 
 
 
 
464a031
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
588f56e
464a031
 
 
 
 
 
 
 
 
 
 
 
588f56e
464a031
7b05996
464a031
 
 
7b05996
 
464a031
82ba732
588f56e
 
b7f2f33
464a031
588f56e
464a031
7b05996
8cc2ce0
464a031
7b05996
588f56e
464a031
7b05996
 
464a031
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
764f692
464a031
 
 
 
 
 
 
 
 
 
 
7234fc1
 
464a031
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cf1a1df
464a031
 
 
7b05996
c147645
 
 
464a031
6793f30
 
7b05996
e0a32e7
464a031
7b05996
c4f3d75
464a031
7b05996
464a031
 
 
 
 
 
 
 
 
7b05996
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.0
model-index:
- name: granite-3.0-1b-a400m-base
  results:
  - task:
      type: text-generation
    dataset:
        type: human-exams
        name: MMLU
    metrics:
    - name: pass@1
      type: pass@1
      value: 25.69
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: human-exams
        name: MMLU-Pro
    metrics:
    - name: pass@1
      type: pass@1
      value: 11.38
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: human-exams
        name: AGI-Eval
    metrics:
    - name: pass@1
      type: pass@1
      value: 19.96
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: commonsense
        name: WinoGrande
    metrics:
    - name: pass@1
      type: pass@1
      value: 62.43
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: commonsense
        name: OBQA 
    metrics:
    - name: pass@1
      type: pass@1
      value: 39.00
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: commonsense
        name: SIQA
    metrics:
    - name: pass@1
      type: pass@1
      value: 35.76
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: commonsense
        name: PIQA
    metrics:
    - name: pass@1
      type: pass@1
      value: 75.35
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: commonsense
        name: Hellaswag
    metrics:
    - name: pass@1
      type: pass@1
      value: 64.92
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: commonsense
        name: TruthfulQA
    metrics:
    - name: pass@1
      type: pass@1
      value: 39.49
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: reading-comprehension
        name: BoolQ
    metrics:
    - name: pass@1
      type: pass@1
      value: 65.44
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: reading-comprehension
        name: SQuAD 2.0
    metrics:
    - name: pass@1
      type: pass@1
      value: 17.78
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: reasoning
        name: ARC-C
    metrics:
    - name: pass@1
      type: pass@1
      value: 38.14
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: reasoning
        name: GPQA
    metrics:
    - name: pass@1
      type: pass@1
      value: 24.41
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: reasoning
        name: BBH
    metrics:
    - name: pass@1
      type: pass@1
      value: 29.84
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: reasoning
        name: MUSR
    metrics:
    - name: pass@1
      type: pass@1
      value: 33.99
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: code
        name: HumanEval
    metrics:
    - name: pass@1
      type: pass@1
      value: 21.95
      veriefied: false
  - task:
      type: text-generation
    dataset:
        type: code
        name: MBPP
    metrics:
    - name: pass@1
      type: pass@1
      value: 23.20
      veriefied: false     
  - task:
      type: text-generation
    dataset:
        type: math
        name: GSM8K
    metrics:
    - name: pass@1
      type: pass@1
      value: 19.26
      veriefied: false 
  - task:
      type: text-generation
    dataset:
        type: math
        name: MATH
    metrics:
    - name: pass@1
      type: pass@1
      value: 8.96
      veriefied: false
---

<!-- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62cd5057674cdb524450093d/1hzxoPwqkBJXshKVVe6_9.png) -->
<!-- ![image/png](granite-3_0-language-models_Group_1.png) -->

# Granite-3.0-1B-A400M-Base

**Model Summary:**
Granite-3.0-1B-A400M-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 8 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.

- **Developers:** Granite Team, IBM
- **GitHub Repository:** [ibm-granite/granite-3.0-language-models](https://github.com/ibm-granite/granite-3.0-language-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** [Granite 3.0 Language Models](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/paper.pdf) 
- **Release Date**: October 21st, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

**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-3.0-1B-A400M-Base model.

Install the following libraries:

```shell
pip install torch torchvision torchaudio
pip install accelerate
pip install transformers
```
Then, copy the code snippet below to run the example.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "auto"
model_path = "ibm-granite/granite-3.0-1b-a400m-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-3.0-1B-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              | 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.

A detailed attribution of datasets can be found in the [Granite Technical Report](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/paper.pdf) and [Accompanying Author List](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/author-ack.pdf).

**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-3.0-1B-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-3.0-1B-A400M-Base model with ethical intentions and in a responsible way. 

<!-- ## Citation
```
@misc{granite-models,
  author = {author 1, author2, ...},
  title = {},
  journal = {},
  volume = {},
  year = {2024},
  url = {https://arxiv.org/abs/0000.00000},
}
``` -->