transformers gguf language granite-3.1 llama-cpp gguf-my-repo base_model:ibm-granite/granite-3.1-2b-base base_model:quantized:ibm-granite/granite-3.1-2b-base license:apache-2.0 endpoints_compatible region:us
---
base_model: ibm-granite/granite-3.1-2b-base
library_name: transformers
license: apache-2.0
tags:
- language
- granite-3.1
- llama-cpp
- gguf-my-repo
---

# Triangle104/granite-3.1-2b-base-Q5_K_S-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.1-2b-base`](https://huggingface.co/ibm-granite/granite-3.1-2b-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ibm-granite/granite-3.1-2b-base) for more details on the model.

---
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.

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/granite-3.1-2b-base-Q5_K_S-GGUF --hf-file granite-3.1-2b-base-q5_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/granite-3.1-2b-base-Q5_K_S-GGUF --hf-file granite-3.1-2b-base-q5_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-Q5_K_S-GGUF --hf-file granite-3.1-2b-base-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/granite-3.1-2b-base-Q5_K_S-GGUF --hf-file granite-3.1-2b-base-q5_k_s.gguf -c 2048
```
