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

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SILMA-9B-Instruct-v1.0-GGUF
File size: 9,273 Bytes
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---
license: gemma
library_name: transformers
pipeline_tag: text-generation
extra_gated_button_content: Acknowledge license
tags:
- conversational
- TensorBlock
- GGUF
language:
- ar
- en
base_model: silma-ai/SILMA-9B-Instruct-v1.0
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
---

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## silma-ai/SILMA-9B-Instruct-v1.0 - GGUF

This repo contains GGUF format model files for [silma-ai/SILMA-9B-Instruct-v1.0](https://huggingface.co/silma-ai/SILMA-9B-Instruct-v1.0).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).


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## Prompt template


```
<bos>{system_prompt}<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [SILMA-9B-Instruct-v1.0-Q2_K.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q2_K.gguf) | Q2_K | 3.544 GB | smallest, significant quality loss - not recommended for most purposes |
| [SILMA-9B-Instruct-v1.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q3_K_S.gguf) | Q3_K_S | 4.040 GB | very small, high quality loss |
| [SILMA-9B-Instruct-v1.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q3_K_M.gguf) | Q3_K_M | 4.435 GB | very small, high quality loss |
| [SILMA-9B-Instruct-v1.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q3_K_L.gguf) | Q3_K_L | 4.780 GB | small, substantial quality loss |
| [SILMA-9B-Instruct-v1.0-Q4_0.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q4_0.gguf) | Q4_0 | 5.069 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [SILMA-9B-Instruct-v1.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q4_K_S.gguf) | Q4_K_S | 5.103 GB | small, greater quality loss |
| [SILMA-9B-Instruct-v1.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q4_K_M.gguf) | Q4_K_M | 5.365 GB | medium, balanced quality - recommended |
| [SILMA-9B-Instruct-v1.0-Q5_0.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q5_0.gguf) | Q5_0 | 6.038 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [SILMA-9B-Instruct-v1.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q5_K_S.gguf) | Q5_K_S | 6.038 GB | large, low quality loss - recommended |
| [SILMA-9B-Instruct-v1.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q5_K_M.gguf) | Q5_K_M | 6.191 GB | large, very low quality loss - recommended |
| [SILMA-9B-Instruct-v1.0-Q6_K.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q6_K.gguf) | Q6_K | 7.068 GB | very large, extremely low quality loss |
| [SILMA-9B-Instruct-v1.0-Q8_0.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/blob/main/SILMA-9B-Instruct-v1.0-Q8_0.gguf) | Q8_0 | 9.152 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/SILMA-9B-Instruct-v1.0-GGUF --include "SILMA-9B-Instruct-v1.0-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/SILMA-9B-Instruct-v1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```