Triangle104/granite-3.1-2b-base-Q8_0-GGUF

This model was converted to GGUF format from ibm-granite/granite-3.1-2b-base using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

Granite-3.1-2B-Base extends the context length of Granite-3.0-2B-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.

Developers: Granite Team, IBM GitHub Repository: ibm-granite/granite-3.1-language-models Website: Granite Docs Paper: Granite 3.1 Language Models (coming soon) Release Date: December 18th, 2024 License: Apache 2.0

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 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 other long-context tasks. 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-3.1-2B-Base model.

Install the following libraries:

pip install torch torchvision torchaudio pip install accelerate pip install transformers

Then, copy the code snippet below to run the example.

from transformers import AutoModelForCausalLM, AutoTokenizer device = "auto" model_path = "ibm-granite/granite-3.1-2b-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-3.1-2B-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.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/granite-3.1-2b-base-Q8_0-GGUF --hf-file granite-3.1-2b-base-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/granite-3.1-2b-base-Q8_0-GGUF --hf-file granite-3.1-2b-base-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/granite-3.1-2b-base-Q8_0-GGUF --hf-file granite-3.1-2b-base-q8_0.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/granite-3.1-2b-base-Q8_0-GGUF --hf-file granite-3.1-2b-base-q8_0.gguf -c 2048