cortecs/Meta-Llama-3-8B-Instruct-GPTQ

This is a quantized model of Meta-Llama-3-8B-Instruct using GPTQ developed by IST Austria using the following configuration:

• 4bit
• 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

Access the model:

curl http://localhost:8000/v1/completions     -H "Content-Type: application/json"     -d ' {
        "model": "cortecs/Meta-Llama-3-8B-Instruct-GPTQ",
        "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	3.96	1887.55
NVIDIA L4x2	4.87	2323.34
NVIDIA L4x4	5.61	2674.18
Performance measured on cortecs inference.