README.md

---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.1
- llama-cpp
- gguf-my-repo
base_model: ibm-granite/granite-3.1-2b-instruct
---

# Triangle104/granite-3.1-2b-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.1-2b-instruct`](https://huggingface.co/ibm-granite/granite-3.1-2b-instruct) 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-instruct) for more details on the model.

---
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.

---
## 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-instruct-Q4_K_M-GGUF --hf-file granite-3.1-2b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/granite-3.1-2b-instruct-Q4_K_M-GGUF --hf-file granite-3.1-2b-instruct-q4_k_m.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-instruct-Q4_K_M-GGUF --hf-file granite-3.1-2b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/granite-3.1-2b-instruct-Q4_K_M-GGUF --hf-file granite-3.1-2b-instruct-q4_k_m.gguf -c 2048
```