README.md · tensorblock/SILMA-9B-Instruct-v1.0-GGUF at main

metadata

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.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

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	Q2_K	3.544 GB	smallest, significant quality loss - not recommended for most purposes
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	Q3_K_M	4.435 GB	very small, high quality loss
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	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	Q4_K_S	5.103 GB	small, greater quality loss
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	Q5_0	6.038 GB	legacy; medium, balanced quality - prefer using Q4_K_M
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	Q5_K_M	6.191 GB	large, very low quality loss - recommended
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	Q8_0	9.152 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

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:

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