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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-base
model-index:
- name: granite-3.0-2b-base
results:
- task:
type: text-generation
dataset:
name: MMLU
type: human-exams
metrics:
- type: pass@1
value: 55.0
name: pass@1
- type: pass@1
value: 23.79
name: pass@1
- type: pass@1
value: 22.56
name: pass@1
- task:
type: text-generation
dataset:
name: WinoGrande
type: commonsense
metrics:
- type: pass@1
value: 74.9
name: pass@1
- type: pass@1
value: 43.0
name: pass@1
- type: pass@1
value: 59.84
name: pass@1
- type: pass@1
value: 79.27
name: pass@1
- type: pass@1
value: 77.65
name: pass@1
- type: pass@1
value: 39.9
name: pass@1
- task:
type: text-generation
dataset:
name: BoolQ
type: reading-comprehension
metrics:
- type: pass@1
value: 81.35
name: pass@1
- type: pass@1
value: 25.22
name: pass@1
- task:
type: text-generation
dataset:
name: ARC-C
type: reasoning
metrics:
- type: pass@1
value: 54.27
name: pass@1
- type: pass@1
value: 30.58
name: pass@1
- type: pass@1
value: 40.69
name: pass@1
- type: pass@1
value: 34.34
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEval
type: code
metrics:
- type: pass@1
value: 38.41
name: pass@1
- type: pass@1
value: 35.4
name: pass@1
- task:
type: text-generation
dataset:
name: GSM8K
type: math
metrics:
- type: pass@1
value: 47.23
name: pass@1
- type: pass@1
value: 19.46
name: pass@1
---
# davelsphere/granite-3.0-2b-base-Q4_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.0-2b-base`](https://huggingface.co/ibm-granite/granite-3.0-2b-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-3.0-2b-base) 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 davelsphere/granite-3.0-2b-base-Q4_K_M-GGUF --hf-file granite-3.0-2b-base-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo davelsphere/granite-3.0-2b-base-Q4_K_M-GGUF --hf-file granite-3.0-2b-base-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 davelsphere/granite-3.0-2b-base-Q4_K_M-GGUF --hf-file granite-3.0-2b-base-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo davelsphere/granite-3.0-2b-base-Q4_K_M-GGUF --hf-file granite-3.0-2b-base-q4_k_m.gguf -c 2048
```
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