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SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF

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SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF
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--


https://colab.research.google.com/drive/1c7oHSEemh8Ih4ExRd4bQ6WHADVY-Mfj1#scrollTo=ADmdVbV93wpy











-
base_model: silma-ai/SILMA-9B-Instruct-v1.0
language:
- ar
- en
library_name: transformers
license: gemma
pipeline_tag: text-generation
tags:
- conversational
- llama-cpp
- gguf-my-repo
extra_gated_button_content: Acknowledge license
model-index:
- name: SILMA-9B-Instruct-v1.0
  results:
  - task:
      type: text-generation
    dataset:
      name: MMLU (Arabic)
      type: OALL/Arabic_MMLU
    metrics:
    - type: loglikelihood_acc_norm
      value: 52.55
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard
      name: Open Arabic LLM Leaderboard
  - task:
      type: text-generation
    dataset:
      name: AlGhafa
      type: OALL/AlGhafa-Arabic-LLM-Benchmark-Native
    metrics:
    - type: loglikelihood_acc_norm
      value: 71.85
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard
      name: Open Arabic LLM Leaderboard
  - task:
      type: text-generation
    dataset:
      name: ARC Challenge (Arabic)
      type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated
    metrics:
    - type: loglikelihood_acc_norm
      value: 78.19
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 86
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 64.05
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 78.89
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 47.64
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 72.93
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 71.96
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 75.55
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 91.26
      name: acc_norm
    - type: loglikelihood_acc_norm
      value: 67.59
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard
      name: Open Arabic LLM Leaderboard
  - task:
      type: text-generation
    dataset:
      name: ACVA
      type: OALL/ACVA
    metrics:
    - type: loglikelihood_acc_norm
      value: 78.89
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard
      name: Open Arabic LLM Leaderboard
  - task:
      type: text-generation
    dataset:
      name: Arabic_EXAMS
      type: OALL/Arabic_EXAMS
    metrics:
    - type: loglikelihood_acc_norm
      value: 51.4
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard
      name: Open Arabic LLM Leaderboard
---

# goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF
This model was converted to GGUF format from [`silma-ai/SILMA-9B-Instruct-v1.0`](https://huggingface.co/silma-ai/SILMA-9B-Instruct-v1.0) 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/silma-ai/SILMA-9B-Instruct-v1.0) 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 goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF --hf-file silma-9b-instruct-v1.0-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF --hf-file silma-9b-instruct-v1.0-iq4_nl-imat.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 goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF --hf-file silma-9b-instruct-v1.0-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo goodasdgood/SILMA-9B-Instruct-v1.0-IQ4_NL-GGUF --hf-file silma-9b-instruct-v1.0-iq4_nl-imat.gguf -c 2048
```