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README.md

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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
+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
+```