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---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.0
- llama-cpp
- gguf-my-repo
base_model: ibm-granite/granite-3.0-2b-instruct
model-index:
- name: granite-3.0-2b-instruct
results:
- task:
type: text-generation
dataset:
name: IFEval
type: instruction-following
metrics:
- type: pass@1
value: 46.07
name: pass@1
- type: pass@1
value: 7.66
name: pass@1
- task:
type: text-generation
dataset:
name: AGI-Eval
type: human-exams
metrics:
- type: pass@1
value: 29.75
name: pass@1
- type: pass@1
value: 56.03
name: pass@1
- type: pass@1
value: 27.92
name: pass@1
- task:
type: text-generation
dataset:
name: OBQA
type: commonsense
metrics:
- type: pass@1
value: 43.2
name: pass@1
- type: pass@1
value: 66.36
name: pass@1
- type: pass@1
value: 76.79
name: pass@1
- type: pass@1
value: 71.9
name: pass@1
- type: pass@1
value: 53.37
name: pass@1
- task:
type: text-generation
dataset:
name: BoolQ
type: reading-comprehension
metrics:
- type: pass@1
value: 84.89
name: pass@1
- type: pass@1
value: 19.73
name: pass@1
- task:
type: text-generation
dataset:
name: ARC-C
type: reasoning
metrics:
- type: pass@1
value: 54.35
name: pass@1
- type: pass@1
value: 28.61
name: pass@1
- type: pass@1
value: 43.74
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis
type: code
metrics:
- type: pass@1
value: 50.61
name: pass@1
- type: pass@1
value: 45.58
name: pass@1
- type: pass@1
value: 51.83
name: pass@1
- type: pass@1
value: 41.0
name: pass@1
- task:
type: text-generation
dataset:
name: GSM8K
type: math
metrics:
- type: pass@1
value: 59.66
name: pass@1
- type: pass@1
value: 23.66
name: pass@1
- task:
type: text-generation
dataset:
name: PAWS-X (7 langs)
type: multilingual
metrics:
- type: pass@1
value: 61.42
name: pass@1
- type: pass@1
value: 37.13
name: pass@1
---
# aashish1904/granite-3.0-2b-instruct-Q2_K-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.0-2b-instruct`](https://huggingface.co/ibm-granite/granite-3.0-2b-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-3.0-2b-instruct) for more details on the model.
## 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 aashish1904/granite-3.0-2b-instruct-Q2_K-GGUF --hf-file granite-3.0-2b-instruct-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo aashish1904/granite-3.0-2b-instruct-Q2_K-GGUF --hf-file granite-3.0-2b-instruct-q2_k.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 aashish1904/granite-3.0-2b-instruct-Q2_K-GGUF --hf-file granite-3.0-2b-instruct-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo aashish1904/granite-3.0-2b-instruct-Q2_K-GGUF --hf-file granite-3.0-2b-instruct-q2_k.gguf -c 2048
```
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