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--- |
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license: gemma |
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library_name: transformers |
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pipeline_tag: text-generation |
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extra_gated_button_content: Acknowledge license |
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tags: |
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- conversational |
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- TensorBlock |
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- GGUF |
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language: |
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- ar |
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- en |
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base_model: silma-ai/SILMA-9B-Instruct-v1.0 |
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model-index: |
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- name: SILMA-9B-Instruct-v1.0 |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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name: MMLU (Arabic) |
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type: OALL/Arabic_MMLU |
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metrics: |
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- type: loglikelihood_acc_norm |
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value: 52.55 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard |
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name: Open Arabic LLM Leaderboard |
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- task: |
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type: text-generation |
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dataset: |
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name: AlGhafa |
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type: OALL/AlGhafa-Arabic-LLM-Benchmark-Native |
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metrics: |
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- type: loglikelihood_acc_norm |
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value: 71.85 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard |
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name: Open Arabic LLM Leaderboard |
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- task: |
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type: text-generation |
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dataset: |
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name: ARC Challenge (Arabic) |
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type: OALL/AlGhafa-Arabic-LLM-Benchmark-Translated |
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metrics: |
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- type: loglikelihood_acc_norm |
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value: 78.19 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 86 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 64.05 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 78.89 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 47.64 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 72.93 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 71.96 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 75.55 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 91.26 |
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name: acc_norm |
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- type: loglikelihood_acc_norm |
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value: 67.59 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard |
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name: Open Arabic LLM Leaderboard |
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- task: |
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type: text-generation |
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dataset: |
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name: ACVA |
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type: OALL/ACVA |
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metrics: |
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- type: loglikelihood_acc_norm |
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value: 78.89 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard |
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name: Open Arabic LLM Leaderboard |
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- task: |
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type: text-generation |
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dataset: |
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name: Arabic_EXAMS |
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type: OALL/Arabic_EXAMS |
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metrics: |
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- type: loglikelihood_acc_norm |
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value: 51.4 |
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name: acc_norm |
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source: |
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url: https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard |
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name: Open Arabic LLM Leaderboard |
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--- |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<div style="display: flex; justify-content: space-between; width: 100%;"> |
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<div style="display: flex; flex-direction: column; align-items: flex-start;"> |
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<p style="margin-top: 0.5em; margin-bottom: 0em;"> |
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Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> |
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</p> |
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</div> |
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</div> |
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## silma-ai/SILMA-9B-Instruct-v1.0 - GGUF |
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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). |
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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 |
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``` |
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<bos>{system_prompt}<start_of_turn>user |
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{prompt}<end_of_turn> |
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<start_of_turn>model |
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``` |
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## Model file specification |
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| Filename | Quant type | File Size | Description | |
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| -------- | ---------- | --------- | ----------- | |
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| [SILMA-9B-Instruct-v1.0-Q2_K.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q2_K.gguf) | Q2_K | 3.544 GB | smallest, significant quality loss - not recommended for most purposes | |
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| [SILMA-9B-Instruct-v1.0-Q3_K_S.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q3_K_S.gguf) | Q3_K_S | 4.040 GB | very small, high quality loss | |
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| [SILMA-9B-Instruct-v1.0-Q3_K_M.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q3_K_M.gguf) | Q3_K_M | 4.435 GB | very small, high quality loss | |
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| [SILMA-9B-Instruct-v1.0-Q3_K_L.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q3_K_L.gguf) | Q3_K_L | 4.780 GB | small, substantial quality loss | |
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| [SILMA-9B-Instruct-v1.0-Q4_0.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/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 | |
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| [SILMA-9B-Instruct-v1.0-Q4_K_S.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q4_K_S.gguf) | Q4_K_S | 5.103 GB | small, greater quality loss | |
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| [SILMA-9B-Instruct-v1.0-Q4_K_M.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q4_K_M.gguf) | Q4_K_M | 5.365 GB | medium, balanced quality - recommended | |
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| [SILMA-9B-Instruct-v1.0-Q5_0.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q5_0.gguf) | Q5_0 | 6.038 GB | legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [SILMA-9B-Instruct-v1.0-Q5_K_S.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q5_K_S.gguf) | Q5_K_S | 6.038 GB | large, low quality loss - recommended | |
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| [SILMA-9B-Instruct-v1.0-Q5_K_M.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q5_K_M.gguf) | Q5_K_M | 6.191 GB | large, very low quality loss - recommended | |
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| [SILMA-9B-Instruct-v1.0-Q6_K.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q6_K.gguf) | Q6_K | 7.068 GB | very large, extremely low quality loss | |
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| [SILMA-9B-Instruct-v1.0-Q8_0.gguf](https://huggingface.co/tensorblock/SILMA-9B-Instruct-v1.0-GGUF/tree/main/SILMA-9B-Instruct-v1.0-Q8_0.gguf) | Q8_0 | 9.152 GB | very large, extremely low quality loss - not recommended | |
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## Downloading instruction |
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### Command line |
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Firstly, install Huggingface Client |
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```shell |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, downoad the individual model file the a local directory |
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```shell |
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huggingface-cli download tensorblock/SILMA-9B-Instruct-v1.0-GGUF --include "SILMA-9B-Instruct-v1.0-Q2_K.gguf" --local-dir MY_LOCAL_DIR |
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``` |
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: |
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```shell |
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huggingface-cli download tensorblock/SILMA-9B-Instruct-v1.0-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' |
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``` |
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