--- license: cc-by-nc-4.0 language: - ro base_model: OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17 datasets: - OpenLLM-Ro/ro_sft_alpaca - OpenLLM-Ro/ro_sft_alpaca_gpt4 - OpenLLM-Ro/ro_sft_dolly - OpenLLM-Ro/ro_sft_selfinstruct_gpt4 - OpenLLM-Ro/ro_sft_norobots - OpenLLM-Ro/ro_sft_orca - OpenLLM-Ro/ro_sft_camel tags: - TensorBlock - GGUF model-index: - name: OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17 results: - task: type: text-generation dataset: name: RoMT-Bench type: RoMT-Bench metrics: - type: Score value: 4.99 name: Score - type: Score value: 5.46 name: First turn - type: Score value: 4.53 name: Second turn - task: type: text-generation dataset: name: RoCulturaBench type: RoCulturaBench metrics: - type: Score value: 3.38 name: Score - task: type: text-generation dataset: name: Romanian_Academic_Benchmarks type: Romanian_Academic_Benchmarks metrics: - type: accuracy value: 52.54 name: Average accuracy - task: type: text-generation dataset: name: OpenLLM-Ro/ro_arc_challenge type: OpenLLM-Ro/ro_arc_challenge metrics: - type: accuracy value: 50.41 name: Average accuracy - type: accuracy value: 47.47 name: 0-shot - type: accuracy value: 48.59 name: 1-shot - type: accuracy value: 50.3 name: 3-shot - type: accuracy value: 51.33 name: 5-shot - type: accuracy value: 52.36 name: 10-shot - type: accuracy value: 52.44 name: 25-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_mmlu type: OpenLLM-Ro/ro_mmlu metrics: - type: accuracy value: 51.61 name: Average accuracy - type: accuracy value: 50.01 name: 0-shot - type: accuracy value: 50.18 name: 1-shot - type: accuracy value: 53.13 name: 3-shot - type: accuracy value: 53.12 name: 5-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_winogrande type: OpenLLM-Ro/ro_winogrande metrics: - type: accuracy value: 66.48 name: Average accuracy - type: accuracy value: 64.96 name: 0-shot - type: accuracy value: 67.09 name: 1-shot - type: accuracy value: 67.01 name: 3-shot - type: accuracy value: 66.85 name: 5-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_hellaswag type: OpenLLM-Ro/ro_hellaswag metrics: - type: accuracy value: 60.27 name: Average accuracy - type: accuracy value: 59.99 name: 0-shot - type: accuracy value: 59.48 name: 1-shot - type: accuracy value: 60.14 name: 3-shot - type: accuracy value: 60.61 name: 5-shot - type: accuracy value: 61.12 name: 10-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_gsm8k type: OpenLLM-Ro/ro_gsm8k metrics: - type: accuracy value: 34.19 name: Average accuracy - type: accuracy value: 21.68 name: 1-shot - type: accuracy value: 38.21 name: 3-shot - type: accuracy value: 42.68 name: 5-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_truthfulqa type: OpenLLM-Ro/ro_truthfulqa metrics: - type: accuracy value: 52.3 name: Average accuracy - task: type: text-generation dataset: name: LaRoSeDa_binary type: LaRoSeDa_binary metrics: - type: macro-f1 value: 97.36 name: Average macro-f1 - type: macro-f1 value: 97.27 name: 0-shot - type: macro-f1 value: 96.37 name: 1-shot - type: macro-f1 value: 97.97 name: 3-shot - type: macro-f1 value: 97.83 name: 5-shot - task: type: text-generation dataset: name: LaRoSeDa_multiclass type: LaRoSeDa_multiclass metrics: - type: macro-f1 value: 67.55 name: Average macro-f1 - type: macro-f1 value: 63.95 name: 0-shot - type: macro-f1 value: 66.89 name: 1-shot - type: macro-f1 value: 68.16 name: 3-shot - type: macro-f1 value: 71.19 name: 5-shot - task: type: text-generation dataset: name: LaRoSeDa_binary_finetuned type: LaRoSeDa_binary_finetuned metrics: - type: macro-f1 value: 98.8 name: Average macro-f1 - task: type: text-generation dataset: name: LaRoSeDa_multiclass_finetuned type: LaRoSeDa_multiclass_finetuned metrics: - type: macro-f1 value: 88.28 name: Average macro-f1 - task: type: text-generation dataset: name: WMT_EN-RO type: WMT_EN-RO metrics: - type: bleu value: 27.93 name: Average bleu - type: bleu value: 24.87 name: 0-shot - type: bleu value: 28.3 name: 1-shot - type: bleu value: 29.26 name: 3-shot - type: bleu value: 29.27 name: 5-shot - task: type: text-generation dataset: name: WMT_RO-EN type: WMT_RO-EN metrics: - type: bleu value: 13.21 name: Average bleu - type: bleu value: 3.69 name: 0-shot - type: bleu value: 5.45 name: 1-shot - type: bleu value: 19.92 name: 3-shot - type: bleu value: 23.8 name: 5-shot - task: type: text-generation dataset: name: WMT_EN-RO_finetuned type: WMT_EN-RO_finetuned metrics: - type: bleu value: 28.72 name: Average bleu - task: type: text-generation dataset: name: WMT_RO-EN_finetuned type: WMT_RO-EN_finetuned metrics: - type: bleu value: 40.86 name: Average bleu - task: type: text-generation dataset: name: XQuAD type: XQuAD metrics: - type: exact_match value: 43.66 name: Average exact_match - type: f1 value: 63.7 name: Average f1 - task: type: text-generation dataset: name: XQuAD_finetuned type: XQuAD_finetuned metrics: - type: exact_match value: 55.04 name: Average exact_match - type: f1 value: 72.31 name: Average f1 - task: type: text-generation dataset: name: STS type: STS metrics: - type: spearman value: 77.43 name: Average spearman - type: pearson value: 78.43 name: Average pearson - task: type: text-generation dataset: name: STS_finetuned type: STS_finetuned metrics: - type: spearman value: 87.25 name: Average spearman - type: pearson value: 87.79 name: Average pearson - task: type: text-generation dataset: name: XQuAD_EM type: XQuAD_EM metrics: - type: exact_match value: 23.36 name: 0-shot - type: exact_match value: 47.98 name: 1-shot - type: exact_match value: 51.85 name: 3-shot - type: exact_match value: 51.43 name: 5-shot - task: type: text-generation dataset: name: XQuAD_F1 type: XQuAD_F1 metrics: - type: f1 value: 46.29 name: 0-shot - type: f1 value: 67.4 name: 1-shot - type: f1 value: 70.58 name: 3-shot - type: f1 value: 70.53 name: 5-shot - task: type: text-generation dataset: name: STS_Spearman type: STS_Spearman metrics: - type: spearman value: 77.91 name: 1-shot - type: spearman value: 77.73 name: 3-shot - type: spearman value: 76.65 name: 5-shot - task: type: text-generation dataset: name: STS_Pearson type: STS_Pearson metrics: - type: pearson value: 78.03 name: 1-shot - type: pearson value: 78.74 name: 3-shot - type: pearson value: 78.53 name: 5-shot ---
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## OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17 - GGUF This repo contains GGUF format model files for [OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17](https://huggingface.co/OpenLLM-Ro/RoMistral-7b-Instruct-2024-05-17). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
Run them on the TensorBlock client using your local machine ↗
## Prompt template ``` {system_prompt} [INST] {prompt} [/INST] ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [RoMistral-7b-Instruct-2024-05-17-Q2_K.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q2_K.gguf) | Q2_K | 2.719 GB | smallest, significant quality loss - not recommended for most purposes | | [RoMistral-7b-Instruct-2024-05-17-Q3_K_S.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q3_K_S.gguf) | Q3_K_S | 3.165 GB | very small, high quality loss | | [RoMistral-7b-Instruct-2024-05-17-Q3_K_M.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q3_K_M.gguf) | Q3_K_M | 3.519 GB | very small, high quality loss | | [RoMistral-7b-Instruct-2024-05-17-Q3_K_L.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q3_K_L.gguf) | Q3_K_L | 3.822 GB | small, substantial quality loss | | [RoMistral-7b-Instruct-2024-05-17-Q4_0.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q4_0.gguf) | Q4_0 | 4.109 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [RoMistral-7b-Instruct-2024-05-17-Q4_K_S.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q4_K_S.gguf) | Q4_K_S | 4.140 GB | small, greater quality loss | | [RoMistral-7b-Instruct-2024-05-17-Q4_K_M.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q4_K_M.gguf) | Q4_K_M | 4.368 GB | medium, balanced quality - recommended | | [RoMistral-7b-Instruct-2024-05-17-Q5_0.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q5_0.gguf) | Q5_0 | 4.998 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [RoMistral-7b-Instruct-2024-05-17-Q5_K_S.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q5_K_S.gguf) | Q5_K_S | 4.998 GB | large, low quality loss - recommended | | [RoMistral-7b-Instruct-2024-05-17-Q5_K_M.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q5_K_M.gguf) | Q5_K_M | 5.131 GB | large, very low quality loss - recommended | | [RoMistral-7b-Instruct-2024-05-17-Q6_K.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q6_K.gguf) | Q6_K | 5.942 GB | very large, extremely low quality loss | | [RoMistral-7b-Instruct-2024-05-17-Q8_0.gguf](https://huggingface.co/tensorblock/RoMistral-7b-Instruct-2024-05-17-GGUF/blob/main/RoMistral-7b-Instruct-2024-05-17-Q8_0.gguf) | Q8_0 | 7.696 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/RoMistral-7b-Instruct-2024-05-17-GGUF --include "RoMistral-7b-Instruct-2024-05-17-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/RoMistral-7b-Instruct-2024-05-17-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```