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---
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'
```
|