|
--- |
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pipeline_tag: text-generation |
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inference: false |
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license: apache-2.0 |
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datasets: |
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- codeparrot/github-code-clean |
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- bigcode/starcoderdata |
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- open-web-math/open-web-math |
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- math-ai/StackMathQA |
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metrics: |
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- code_eval |
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library_name: transformers |
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tags: |
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- code |
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- granite |
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- llama-cpp |
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- gguf-my-repo |
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base_model: ibm-granite/granite-3b-code-base-2k |
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model-index: |
|
- name: granite-3b-code-base-2k |
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results: |
|
- task: |
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type: text-generation |
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dataset: |
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name: MBPP |
|
type: mbpp |
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metrics: |
|
- type: pass@1 |
|
value: 36.0 |
|
name: pass@1 |
|
- task: |
|
type: text-generation |
|
dataset: |
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name: MBPP+ |
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type: evalplus/mbppplus |
|
metrics: |
|
- type: pass@1 |
|
value: 45.1 |
|
name: pass@1 |
|
- task: |
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type: text-generation |
|
dataset: |
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name: HumanEvalSynthesis(Python) |
|
type: bigcode/humanevalpack |
|
metrics: |
|
- type: pass@1 |
|
value: 36.6 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 37.2 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 40.9 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 26.2 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 35.4 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 22.0 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 25.0 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 18.9 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 29.9 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 17.1 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 26.8 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 14.0 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 18.3 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 23.2 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 29.9 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 24.4 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 16.5 |
|
name: pass@1 |
|
- type: pass@1 |
|
value: 3.7 |
|
name: pass@1 |
|
--- |
|
|
|
# AIronMind/granite-3b-code-base-2k-Q4_K_M-GGUF |
|
This model was converted to GGUF format from [`ibm-granite/granite-3b-code-base-2k`](https://huggingface.co/ibm-granite/granite-3b-code-base-2k) 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-2k) 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-2k-Q4_K_M-GGUF --hf-file granite-3b-code-base-2k-q4_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
|
|
|
### Server: |
|
```bash |
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llama-server --hf-repo AIronMind/granite-3b-code-base-2k-Q4_K_M-GGUF --hf-file granite-3b-code-base-2k-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 |
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``` |
|
|
|
Step 3: Run inference through the main binary. |
|
``` |
|
./llama-cli --hf-repo AIronMind/granite-3b-code-base-2k-Q4_K_M-GGUF --hf-file granite-3b-code-base-2k-q4_k_m.gguf -p "The meaning to life and the universe is" |
|
``` |
|
or |
|
``` |
|
./llama-server --hf-repo AIronMind/granite-3b-code-base-2k-Q4_K_M-GGUF --hf-file granite-3b-code-base-2k-q4_k_m.gguf -c 2048 |
|
``` |
|
|
