ibm-granite_-_granite-3.0-2b-instruct-gguf

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granite-3.0-2b-instruct - GGUF

• Model creator: https://huggingface.co/ibm-granite/
• Original model: https://huggingface.co/ibm-granite/granite-3.0-2b-instruct/

Original model description:

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: 46.07 veriefied: false
  • task: type: text-generation dataset: type: instruction-following name: MT-Bench metrics:
    • name: pass@1 type: pass@1 value: 7.66 veriefied: false
  • task: type: text-generation dataset: type: human-exams name: AGI-Eval metrics:
    • name: pass@1 type: pass@1 value: 29.75 veriefied: false
  • task: type: text-generation dataset: type: human-exams name: MMLU metrics:
    • name: pass@1 type: pass@1 value: 56.03 veriefied: false
  • task: type: text-generation dataset: type: human-exams name: MMLU-Pro metrics:
    • name: pass@1 type: pass@1 value: 27.92 veriefied: false
  • task: type: text-generation dataset: type: commonsense name: OBQA metrics:
    • name: pass@1 type: pass@1 value: 43.20 veriefied: false
  • task: type: text-generation dataset: type: commonsense name: SIQA metrics:
    • name: pass@1 type: pass@1 value: 66.36 veriefied: false
  • task: type: text-generation dataset: type: commonsense name: Hellaswag metrics:
    • name: pass@1 type: pass@1 value: 76.79 veriefied: false
  • task: type: text-generation dataset: type: commonsense name: WinoGrande metrics:
    • name: pass@1 type: pass@1 value: 71.90 veriefied: false
  • task: type: text-generation dataset: type: commonsense name: TruthfulQA metrics:
    • name: pass@1 type: pass@1 value: 53.37 veriefied: false
  • task: type: text-generation dataset: type: reading-comprehension name: BoolQ metrics:
    • name: pass@1 type: pass@1 value: 84.89 veriefied: false
  • task: type: text-generation dataset: type: reading-comprehension name: SQuAD 2.0 metrics:
    • name: pass@1 type: pass@1 value: 19.73 veriefied: false
  • task: type: text-generation dataset: type: reasoning name: ARC-C metrics:
    • name: pass@1 type: pass@1 value: 54.35 veriefied: false
  • task: type: text-generation dataset: type: reasoning name: GPQA metrics:
    • name: pass@1 type: pass@1 value: 28.61 veriefied: false
  • task: type: text-generation dataset: type: reasoning name: BBH metrics:
    • name: pass@1 type: pass@1 value: 43.74 veriefied: false
  • task: type: text-generation dataset: type: code name: HumanEvalSynthesis metrics:
    • name: pass@1 type: pass@1 value: 50.61 veriefied: false
  • task: type: text-generation dataset: type: code name: HumanEvalExplain metrics:
    • name: pass@1 type: pass@1 value: 45.58 veriefied: false
  • task: type: text-generation dataset: type: code name: HumanEvalFix metrics:
    • name: pass@1 type: pass@1 value: 51.83 veriefied: false
  • task: type: text-generation dataset: type: code name: MBPP metrics:
    • name: pass@1 type: pass@1 value: 41.00 veriefied: false
  • task: type: text-generation dataset: type: math name: GSM8K metrics:
    • name: pass@1 type: pass@1 value: 59.66 veriefied: false
  • task: type: text-generation dataset: type: math name: MATH metrics:
    • name: pass@1 type: pass@1 value: 23.66 veriefied: false
  • task: type: text-generation dataset: type: multilingual name: PAWS-X (7 langs) metrics:
    • name: pass@1 type: pass@1 value: 61.42 veriefied: false
  • task: type: text-generation dataset: type: multilingual name: MGSM (6 langs) metrics:
    • name: pass@1 type: pass@1 value: 37.13 veriefied: false

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Granite-3.0-2B-Instruct

Model Summary: Granite-3.0-2B-Instruct is a 2B parameter model finetuned from Granite-3.0-2B-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-3.0-2B-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-3.0-2b-instruct"
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-3.0-2B-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.

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.

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