pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.0
model-index:
- name: granite-3.0-2b-instruct
results:
- task:
type: text-generation
dataset:
type: instruction-following
name: IFEval
metrics:
- name: pass@1
type: pass@1
value: 52.27
veriefied: false
- task:
type: text-generation
dataset:
type: instruction-following
name: MT-Bench
metrics:
- name: pass@1
type: pass@1
value: 8.22
veriefied: false
- task:
type: text-generation
dataset:
type: human-exams
name: AGI-Eval
metrics:
- name: pass@1
type: pass@1
value: 40.52
veriefied: false
- task:
type: text-generation
dataset:
type: human-exams
name: MMLU
metrics:
- name: pass@1
type: pass@1
value: 65.82
veriefied: false
- task:
type: text-generation
dataset:
type: human-exams
name: MMLU-Pro
metrics:
- name: pass@1
type: pass@1
value: 34.45
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: OBQA
metrics:
- name: pass@1
type: pass@1
value: 46.6
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: SIQA
metrics:
- name: pass@1
type: pass@1
value: 71.21
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: Hellaswag
metrics:
- name: pass@1
type: pass@1
value: 82.61
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: WinoGrande
metrics:
- name: pass@1
type: pass@1
value: 77.51
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: TruthfulQA
metrics:
- name: pass@1
type: pass@1
value: 60.32
veriefied: false
- task:
type: text-generation
dataset:
type: reading-comprehension
name: BoolQ
metrics:
- name: pass@1
type: pass@1
value: 88.65
veriefied: false
- task:
type: text-generation
dataset:
type: reading-comprehension
name: SQuAD 2.0
metrics:
- name: pass@1
type: pass@1
value: 21.58
veriefied: false
- task:
type: text-generation
dataset:
type: reasoning
name: ARC-C
metrics:
- name: pass@1
type: pass@1
value: 64.16
veriefied: false
- task:
type: text-generation
dataset:
type: reasoning
name: GPQA
metrics:
- name: pass@1
type: pass@1
value: 33.81
veriefied: false
- task:
type: text-generation
dataset:
type: reasoning
name: BBH
metrics:
- name: pass@1
type: pass@1
value: 51.55
veriefied: false
- task:
type: text-generation
dataset:
type: code
name: HumanEvalSynthesis
metrics:
- name: pass@1
type: pass@1
value: 64.63
veriefied: false
- task:
type: text-generation
dataset:
type: code
name: HumanEvalExplain
metrics:
- name: pass@1
type: pass@1
value: 57.16
veriefied: false
- task:
type: text-generation
dataset:
type: code
name: HumanEvalFix
metrics:
- name: pass@1
type: pass@1
value: 65.85
veriefied: false
- task:
type: text-generation
dataset:
type: code
name: MBPP
metrics:
- name: pass@1
type: pass@1
value: 49.6
veriefied: false
- task:
type: text-generation
dataset:
type: math
name: GSM8K
metrics:
- name: pass@1
type: pass@1
value: 68.99
veriefied: false
- task:
type: text-generation
dataset:
type: math
name: MATH
metrics:
- name: pass@1
type: pass@1
value: 30.94
veriefied: false
- task:
type: text-generation
dataset:
type: multilingual
name: PAWS-X (7 langs)
metrics:
- name: pass@1
type: pass@1
value: 64.94
veriefied: false
- task:
type: text-generation
dataset:
type: multilingual
name: MGSM (6 langs)
metrics:
- name: pass@1
type: pass@1
value: 48.2
veriefied: false
base_model:
- ibm-granite/granite-3.0-8b-instruct
Disclaimer and Requirements
This model is a clone of ibm-granite/granite-3.0-8b-instruct compressed using ZipNN. Compressed losslessly to 67% its original size, ZipNN saved ~6GB in storage and potentially ~9TB in data transfer monthly.
Requirement
In order to use the model, ZipNN is necessary:
pip install zipnn
Use This Model
# Use a pipeline as a high-level helper
from transformers import pipeline
from zipnn import zipnn_hf
zipnn_hf()
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="royleibov/granite-3.0-8b-instruct-ZipNN-Compressed")
pipe(messages)
# Load model directly
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from zipnn import zipnn_hf
zipnn_hf()
model = AutoModelForCausalLM.from_pretrained(
"royleibov/granite-3.0-8b-instruct-ZipNN-Compressed",
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("royleibov/granite-3.0-8b-instruct-ZipNN-Compressed")
ZipNN
ZipNN also allows you to seemlessly save local disk space in your cache after the model is downloaded.
To compress the cached model, simply run:
python zipnn_compress_path.py safetensors --model royleibov/granite-3.0-8b-instruct-ZipNN-Compressed --hf_cache
The model will be decompressed automatically and safely as long as zipnn_hf() is added at the top of the file like in the example above.
To decompress manualy, simply run:
python zipnn_decompress_path.py --model royleibov/granite-3.0-8b-instruct-ZipNN-Compressed --hf_cache
Granite-3.0-8B-Instruct
Model Summary: Granite-3.0-8B-Instruct is a 8B parameter model finetuned from Granite-3.0-8B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. 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-3.0-language-models
- Website: Granite Docs
- Paper: Granite 3.0 Language Models
- Release Date: October 21st, 2024
- License: Apache 2.0
Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 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
Generation: This is a simple example of how to use Granite-3.0-8B-Instruct model.
Install the following libraries:
pip install torch torchvision torchaudio
pip install accelerate
pip install transformers
pip install zipnn
Then, copy the snippet from the section that is relevant for your use case.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from zipnn import zipnn_hf
zipnn_hf()
device = "auto"
model_path = "royleibov/granite-3.0-8b-instruct-ZipNN-Compressed"
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
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)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens,
max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output)
Model Architecture: Granite-3.0-8B-Instruct 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 |
| # Paremeters | 2.5B | 8.1B | 1.3B | 3.3B |
| # Active Parameters | 2.5B | 8.1B | 400M | 800M |
| # Training tokens | 12T | 12T | 10T | 10T |
Training Data: Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities, and (3) very small amounts of human-curated data. A detailed attribution of datasets can be found in the Granite Technical Report and Accompanying Author List.
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: Granite 3.0 Instruct Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering eleven languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks.