---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- granite-3.0
- llama-cpp
- gguf-my-repo
new_version: ibm-granite/granite-3.1-1b-a400m-base
base_model: ibm-granite/granite-3.0-1b-a400m-base
model-index:
- name: granite-3.0-1b-a400m-base
  results:
  - task:
      type: text-generation
    dataset:
      name: MMLU
      type: human-exams
    metrics:
    - type: pass@1
      value: 25.69
      name: pass@1
    - type: pass@1
      value: 11.38
      name: pass@1
    - type: pass@1
      value: 19.96
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: WinoGrande
      type: commonsense
    metrics:
    - type: pass@1
      value: 62.43
      name: pass@1
    - type: pass@1
      value: 39
      name: pass@1
    - type: pass@1
      value: 35.76
      name: pass@1
    - type: pass@1
      value: 75.35
      name: pass@1
    - type: pass@1
      value: 64.92
      name: pass@1
    - type: pass@1
      value: 39.49
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: BoolQ
      type: reading-comprehension
    metrics:
    - type: pass@1
      value: 65.44
      name: pass@1
    - type: pass@1
      value: 17.78
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: ARC-C
      type: reasoning
    metrics:
    - type: pass@1
      value: 38.14
      name: pass@1
    - type: pass@1
      value: 24.41
      name: pass@1
    - type: pass@1
      value: 29.84
      name: pass@1
    - type: pass@1
      value: 33.99
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: HumanEval
      type: code
    metrics:
    - type: pass@1
      value: 21.95
      name: pass@1
    - type: pass@1
      value: 23.2
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: GSM8K
      type: math
    metrics:
    - type: pass@1
      value: 19.26
      name: pass@1
    - type: pass@1
      value: 8.96
      name: pass@1
---

# AIronMind/granite-3.0-1b-a400m-base-Q4_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3.0-1b-a400m-base`](https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-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-3.0-1b-a400m-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 AIronMind/granite-3.0-1b-a400m-base-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-base-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo AIronMind/granite-3.0-1b-a400m-base-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-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 AIronMind/granite-3.0-1b-a400m-base-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-base-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo AIronMind/granite-3.0-1b-a400m-base-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-base-q4_k_m.gguf -c 2048
```