metadata
datasets: wikitext
license: other
license_link: https://llama.meta.com/llama3/license/
This is a quantized model of Meta-Llama-3-8B-Instruct using GPTQ developed by IST Austria using the following configuration:
- 8bit
- Act order: True
- Group size: 128
Usage
Install vLLM and run the server:
python -m vllm.entrypoints.openai.api_server --model cortecs/Meta-Llama-3-8B-Instruct-GPTQ-8b
Access the model:
curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d ' {
"model": "cortecs/Meta-Llama-3-8B-Instruct-GPTQ-8b",
"prompt": "San Francisco is a"
} '
Evaluations
English | Meta-Llama-3-8B-Instruct | Meta-Llama-3-8B-Instruct-GPTQ-8b | Meta-Llama-3-8B-Instruct-GPTQ |
---|---|---|---|
Avg. | 66.97 | 67.0 | 63.52 |
ARC | 62.5 | 62.5 | 54.6 |
Hellaswag | 70.3 | 70.3 | 69.5 |
MMLU | 68.11 | 68.21 | 66.46 |
French | Meta-Llama-3-8B-Instruct | Meta-Llama-3-8B-Instruct-GPTQ-8b | Meta-Llama-3-8B-Instruct-GPTQ |
Avg. | 57.73 | 57.7 | 53.33 |
Hellaswag_fr | 61.7 | 62.2 | 59.3 |
ARC_fr | 53.3 | 53.1 | 46.4 |
MMLU_fr | 58.2 | 57.8 | 54.3 |
German | Meta-Llama-3-8B-Instruct | Meta-Llama-3-8B-Instruct-GPTQ-8b | Meta-Llama-3-8B-Instruct-GPTQ |
Avg. | 53.47 | 53.67 | 49.0 |
ARC_de | 49.1 | 49.0 | 41.6 |
Hellaswag_de | 55.0 | 55.2 | 53.3 |
MMLU_de | 56.3 | 56.8 | 52.1 |
Italian | Meta-Llama-3-8B-Instruct | Meta-Llama-3-8B-Instruct-GPTQ-8b | Meta-Llama-3-8B-Instruct-GPTQ |
Avg. | 56.73 | 56.67 | 51.3 |
Hellaswag_it | 61.3 | 61.3 | 58.4 |
MMLU_it | 57.3 | 57.0 | 53.0 |
ARC_it | 51.6 | 51.7 | 42.5 |
Safety | Meta-Llama-3-8B-Instruct | Meta-Llama-3-8B-Instruct-GPTQ-8b | Meta-Llama-3-8B-Instruct-GPTQ |
Avg. | 61.42 | 61.42 | 61.53 |
RealToxicityPrompts | 97.2 | 97.2 | 97.2 |
TruthfulQA | 51.65 | 51.58 | 51.98 |
CrowS | 35.42 | 35.48 | 35.42 |
Spanish | Meta-Llama-3-8B-Instruct | Meta-Llama-3-8B-Instruct-GPTQ-8b | Meta-Llama-3-8B-Instruct-GPTQ |
Avg. | 59 | 58.63 | 54.6 |
ARC_es | 54.1 | 53.8 | 46.9 |
Hellaswag_es | 63.8 | 63.3 | 60.3 |
MMLU_es | 59.1 | 58.8 | 56.6 |
We did not check for data contamination.
Evaluation was done using Eval. Harness using limit=1000
.
Performance
requests/s | tokens/s | |
---|---|---|
NVIDIA L4x1 | 2.75 | 1312.26 |
NVIDIA L4x2 | 4.36 | 2080.17 |
NVIDIA L4x4 | 5.33 | 2539.76 |
Performance measured on cortecs inference. |