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---
pipeline_tag: text-generation
inference: false
license: apache-2.0
datasets:
- codeparrot/github-code-clean
- bigcode/starcoderdata
- open-web-math/open-web-math
- math-ai/StackMathQA
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
- llama-cpp
- gguf-my-repo
base_model: ibm-granite/granite-3b-code-base-128k
model-index:
- name: granite-3b-code-base-128k
results:
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis (Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 36.0
name: pass@1
verified: false
- type: pass@1
value: 30.5
name: pass@1
verified: false
- type: pass@1
value: 22.4
name: pass@1
verified: false
- type: pass@1
value: 19.9
name: pass@1
verified: false
- task:
type: text-generation
dataset:
name: RepoQA (Python@16K)
type: repoqa
metrics:
- type: pass@1 (thresh=0.5)
value: 40.0
name: pass@1 (thresh=0.5)
verified: false
- type: pass@1 (thresh=0.5)
value: 36.0
name: pass@1 (thresh=0.5)
verified: false
- type: pass@1 (thresh=0.5)
value: 37.0
name: pass@1 (thresh=0.5)
verified: false
- type: pass@1 (thresh=0.5)
value: 27.0
name: pass@1 (thresh=0.5)
verified: false
- type: pass@1 (thresh=0.5)
value: 29.0
name: pass@1 (thresh=0.5)
verified: false
- task:
type: text-generation
dataset:
name: LCC (Balanced)
type: lcc
metrics:
- type: Exact Match@4K
value: 54.6
name: Exact Match@4K
verified: false
- type: Exact Match@8K
value: 56.8
name: Exact Match@8K
verified: false
- type: Exact Match@16K
value: 52.2
name: Exact Match@16K
verified: false
- type: Exact Match@32K
value: 57.8
name: Exact Match@32K
verified: false
- task:
type: text-generation
dataset:
name: RepoBench-P (Balanced)
type: repobench
metrics:
- type: Exact Match@4K
value: 39.8
name: Exact Match@4K
verified: false
- type: Exact Match@8K
value: 46.8
name: Exact Match@8K
verified: false
- type: Exact Match@16K
value: 43.1
name: Exact Match@16K
verified: false
- type: Exact Match@32K
value: 45.3
name: Exact Match@32K
verified: false
---
# AIronMind/granite-3b-code-base-128k-Q4_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-3b-code-base-128k`](https://huggingface.co/ibm-granite/granite-3b-code-base-128k) 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-3b-code-base-128k) 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-3b-code-base-128k-Q4_K_M-GGUF --hf-file granite-3b-code-base-128k-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo AIronMind/granite-3b-code-base-128k-Q4_K_M-GGUF --hf-file granite-3b-code-base-128k-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-3b-code-base-128k-Q4_K_M-GGUF --hf-file granite-3b-code-base-128k-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo AIronMind/granite-3b-code-base-128k-Q4_K_M-GGUF --hf-file granite-3b-code-base-128k-q4_k_m.gguf -c 2048
```