Upload folder using huggingface_hub

.gitattributes

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+RoMistral-7b-Instruct-2024-05-17-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
+RoMistral-7b-Instruct-2024-05-17-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,476 @@
+---
+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
+---
+
+<div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
+</div>
+<div style="display: flex; justify-content: space-between; width: 100%;">
+    <div style="display: flex; flex-direction: column; align-items: flex-start;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;">
+            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>
+        </p>
+    </div>
+</div>
+
+## 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).
+
+<div style="text-align: left; margin: 20px 0;">
+    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
+        Run them on the TensorBlock client using your local machine ↗
+    </a>
+</div>
+
+## Prompt template
+
+```
+<s>{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'
+```

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