Triangle104/granite-3.1-2b-instruct-Q5_K_S-GGUF

This model was converted to GGUF format from ibm-granite/granite-3.1-2b-instruct 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-Instruct is a 2B parameter long-context instruct model finetuned from Granite-3.1-2B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. 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-3.1-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: 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 Long-context tasks including long document/meeting summarization, long document QA, etc.

Generation: This is a simple example of how to use Granite-3.1-2B-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-3.1-2b-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-3.1-2B-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.

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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-instruct-Q5_K_S-GGUF --hf-file granite-3.1-2b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/granite-3.1-2b-instruct-Q5_K_S-GGUF --hf-file granite-3.1-2b-instruct-q5_k_s.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-instruct-Q5_K_S-GGUF --hf-file granite-3.1-2b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/granite-3.1-2b-instruct-Q5_K_S-GGUF --hf-file granite-3.1-2b-instruct-q5_k_s.gguf -c 2048