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---
license: cc-by-nc-4.0
language:
- ro
base_model: OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09
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
- OpenLLM-Ro/ro_sft_oasst
- OpenLLM-Ro/ro_sft_ultrachat
tags:
- llama-cpp
- gguf-my-repo
model-index:
- name: OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09
results:
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- type: Score
value: 5.42
name: Score
- type: Score
value: 5.95
name: First turn
- type: Score
value: 4.89
name: Second turn
- task:
type: text-generation
dataset:
name: RoCulturaBench
type: RoCulturaBench
metrics:
- type: Score
value: 3.55
name: Score
- task:
type: text-generation
dataset:
name: Romanian_Academic_Benchmarks
type: Romanian_Academic_Benchmarks
metrics:
- type: accuracy
value: 53.03
name: Average accuracy
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- type: accuracy
value: 47.69
name: Average accuracy
- type: accuracy
value: 42.76
name: 0-shot
- type: accuracy
value: 46.44
name: 1-shot
- type: accuracy
value: 48.24
name: 3-shot
- type: accuracy
value: 48.84
name: 5-shot
- type: accuracy
value: 49.36
name: 10-shot
- type: accuracy
value: 50.47
name: 25-shot
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- type: accuracy
value: 54.57
name: Average accuracy
- type: accuracy
value: 52.95
name: 0-shot
- type: accuracy
value: 54.62
name: 1-shot
- type: accuracy
value: 55.54
name: 3-shot
- type: accuracy
value: 55.17
name: 5-shot
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- type: accuracy
value: 65.84
name: Average accuracy
- type: accuracy
value: 64.4
name: 0-shot
- type: accuracy
value: 66.14
name: 1-shot
- type: accuracy
value: 65.75
name: 3-shot
- type: accuracy
value: 67.09
name: 5-shot
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- type: accuracy
value: 59.94
name: Average accuracy
- type: accuracy
value: 59.07
name: 0-shot
- type: accuracy
value: 59.26
name: 1-shot
- type: accuracy
value: 60.41
name: 3-shot
- type: accuracy
value: 60.18
name: 5-shot
- type: accuracy
value: 60.77
name: 10-shot
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- type: accuracy
value: 44.3
name: Average accuracy
- type: accuracy
value: 35.1
name: 1-shot
- type: accuracy
value: 47.01
name: 3-shot
- type: accuracy
value: 50.8
name: 5-shot
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_truthfulqa
type: OpenLLM-Ro/ro_truthfulqa
metrics:
- type: accuracy
value: 45.82
name: Average accuracy
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- type: macro-f1
value: 94.56
name: Average macro-f1
- type: macro-f1
value: 90.18
name: 0-shot
- type: macro-f1
value: 94.45
name: 1-shot
- type: macro-f1
value: 96.36
name: 3-shot
- type: macro-f1
value: 97.27
name: 5-shot
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- type: macro-f1
value: 60.1
name: Average macro-f1
- type: macro-f1
value: 67.56
name: 0-shot
- type: macro-f1
value: 63.21
name: 1-shot
- type: macro-f1
value: 51.69
name: 3-shot
- type: macro-f1
value: 57.95
name: 5-shot
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary_finetuned
type: LaRoSeDa_binary_finetuned
metrics:
- type: macro-f1
value: 95.12
name: Average macro-f1
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass_finetuned
type: LaRoSeDa_multiclass_finetuned
metrics:
- type: macro-f1
value: 87.53
name: Average macro-f1
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- type: bleu
value: 21.88
name: Average bleu
- type: bleu
value: 5.12
name: 0-shot
- type: bleu
value: 26.99
name: 1-shot
- type: bleu
value: 27.91
name: 3-shot
- type: bleu
value: 27.51
name: 5-shot
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- type: bleu
value: 23.99
name: Average bleu
- type: bleu
value: 1.63
name: 0-shot
- type: bleu
value: 22.59
name: 1-shot
- type: bleu
value: 35.7
name: 3-shot
- type: bleu
value: 36.05
name: 5-shot
- task:
type: text-generation
dataset:
name: WMT_EN-RO_finetuned
type: WMT_EN-RO_finetuned
metrics:
- type: bleu
value: 28.27
name: Average bleu
- task:
type: text-generation
dataset:
name: WMT_RO-EN_finetuned
type: WMT_RO-EN_finetuned
metrics:
- type: bleu
value: 40.44
name: Average bleu
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- type: exact_match
value: 13.59
name: Average exact_match
- type: f1
value: 23.56
name: Average f1
- task:
type: text-generation
dataset:
name: XQuAD_finetuned
type: XQuAD_finetuned
metrics:
- type: exact_match
value: 49.41
name: Average exact_match
- type: f1
value: 62.93
name: Average f1
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- type: spearman
value: 75.89
name: Average spearman
- type: pearson
value: 76.0
name: Average pearson
- task:
type: text-generation
dataset:
name: STS_finetuned
type: STS_finetuned
metrics:
- type: spearman
value: 86.86
name: Average spearman
- type: pearson
value: 87.05
name: Average pearson
- task:
type: text-generation
dataset:
name: XQuAD_EM
type: XQuAD_EM
metrics:
- type: exact_match
value: 6.55
name: 0-shot
- type: exact_match
value: 38.32
name: 1-shot
- type: exact_match
value: 8.66
name: 3-shot
- type: exact_match
value: 0.84
name: 5-shot
- task:
type: text-generation
dataset:
name: XQuAD_F1
type: XQuAD_F1
metrics:
- type: f1
value: 16.04
name: 0-shot
- type: f1
value: 56.16
name: 1-shot
- type: f1
value: 15.64
name: 3-shot
- type: f1
value: 6.39
name: 5-shot
- task:
type: text-generation
dataset:
name: STS_Spearman
type: STS_Spearman
metrics:
- type: spearman
value: 76.27
name: 1-shot
- type: spearman
value: 75.48
name: 3-shot
- type: spearman
value: 75.92
name: 5-shot
- task:
type: text-generation
dataset:
name: STS_Pearson
type: STS_Pearson
metrics:
- type: pearson
value: 76.76
name: 1-shot
- type: pearson
value: 75.38
name: 3-shot
- type: pearson
value: 75.87
name: 5-shot
---
# chrisgru/RoLlama3.1-8b-Instruct-2024-10-09-Q8_0-GGUF
This model was converted to GGUF format from [`OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09`](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2024-10-09) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo chrisgru/RoLlama3.1-8b-Instruct-2024-10-09-Q8_0-GGUF --hf-file rollama3.1-8b-instruct-2024-10-09-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo chrisgru/RoLlama3.1-8b-Instruct-2024-10-09-Q8_0-GGUF --hf-file rollama3.1-8b-instruct-2024-10-09-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo chrisgru/RoLlama3.1-8b-Instruct-2024-10-09-Q8_0-GGUF --hf-file rollama3.1-8b-instruct-2024-10-09-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo chrisgru/RoLlama3.1-8b-Instruct-2024-10-09-Q8_0-GGUF --hf-file rollama3.1-8b-instruct-2024-10-09-q8_0.gguf -c 2048
```
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