README.md

---
base_model: ibm-granite/granite-8b-code-instruct-4k
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
library_name: transformers
license: apache-2.0
metrics:
- code_eval
pipeline_tag: text-generation
tags:
- code
- granite
- llama-cpp
- gguf-my-repo
inference: false
model-index:
- name: granite-8b-code-instruct-4k
  results:
  - task:
      type: text-generation
    dataset:
      name: HumanEvalSynthesis(Python)
      type: bigcode/humanevalpack
    metrics:
    - type: pass@1
      value: 57.9
      name: pass@1
    - type: pass@1
      value: 52.4
      name: pass@1
    - type: pass@1
      value: 58.5
      name: pass@1
    - type: pass@1
      value: 43.3
      name: pass@1
    - type: pass@1
      value: 48.2
      name: pass@1
    - type: pass@1
      value: 37.2
      name: pass@1
    - type: pass@1
      value: 53.0
      name: pass@1
    - type: pass@1
      value: 42.7
      name: pass@1
    - type: pass@1
      value: 52.4
      name: pass@1
    - type: pass@1
      value: 36.6
      name: pass@1
    - type: pass@1
      value: 43.9
      name: pass@1
    - type: pass@1
      value: 16.5
      name: pass@1
    - type: pass@1
      value: 39.6
      name: pass@1
    - type: pass@1
      value: 40.9
      name: pass@1
    - type: pass@1
      value: 48.2
      name: pass@1
    - type: pass@1
      value: 41.5
      name: pass@1
    - type: pass@1
      value: 39.0
      name: pass@1
    - type: pass@1
      value: 32.9
      name: pass@1
---

# Triangle104/granite-8b-code-instruct-4k-Q4_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-8b-code-instruct-4k`](https://huggingface.co/ibm-granite/granite-8b-code-instruct-4k) 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-8b-code-instruct-4k) 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 Triangle104/granite-8b-code-instruct-4k-Q4_K_M-GGUF --hf-file granite-8b-code-instruct-4k-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/granite-8b-code-instruct-4k-Q4_K_M-GGUF --hf-file granite-8b-code-instruct-4k-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-8b-code-instruct-4k-Q4_K_M-GGUF --hf-file granite-8b-code-instruct-4k-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/granite-8b-code-instruct-4k-Q4_K_M-GGUF --hf-file granite-8b-code-instruct-4k-q4_k_m.gguf -c 2048
```