cortecs/Meta-Llama-3-70B-Instruct-GPTQ

This is a quantized model of Meta-Llama-3-70B-Instruct.yaml 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-3-70B-Instruct-GPTQ

Access the model:

curl http://localhost:8000/v1/completions     -H "Content-Type: application/json"     -d ' {
        "model": "cortecs/Meta-Llama-3-70B-Instruct-GPTQ",
        "prompt": "San Francisco is a"
    } '

Evaluations

English	Meta-Llama-3-70B-Instruct	Meta-Llama-3-70B-Instruct-GPTQ-8b	Meta-Llama-3-70B-Instruct-GPTQ
Avg.	76.19	76.16	75.14
ARC	71.6	71.4	70.7
Hellaswag	77.3	77.1	76.4
MMLU	79.66	79.98	78.33
French	Meta-Llama-3-70B-Instruct	Meta-Llama-3-70B-Instruct-GPTQ-8b	Meta-Llama-3-70B-Instruct-GPTQ
Avg.	70.97	71.03	70.27
ARC_fr	65.0	65.3	64.7
Hellaswag_fr	72.4	72.4	71.4
MMLU_fr	75.5	75.4	74.7
German	Meta-Llama-3-70B-Instruct	Meta-Llama-3-70B-Instruct-GPTQ-8b	Meta-Llama-3-70B-Instruct-GPTQ
Avg.	68.43	68.37	66.93
ARC_de	64.2	64.3	62.6
Hellaswag_de	67.8	67.7	66.7
MMLU_de	73.3	73.1	71.5
Italian	Meta-Llama-3-70B-Instruct	Meta-Llama-3-70B-Instruct-GPTQ-8b	Meta-Llama-3-70B-Instruct-GPTQ
Avg.	70.17	70.43	68.63
ARC_it	64.0	64.3	62.1
Hellaswag_it	72.6	72.4	71.0
MMLU_it	73.9	74.6	72.8
Safety	Meta-Llama-3-70B-Instruct	Meta-Llama-3-70B-Instruct-GPTQ-8b	Meta-Llama-3-70B-Instruct-GPTQ
Avg.	64.28	64.17	63.64
RealToxicityPrompts	97.9	97.8	98.1
TruthfulQA	61.91	61.67	59.91
CrowS	33.04	33.04	32.92
Spanish	Meta-Llama-3-70B-Instruct	Meta-Llama-3-70B-Instruct-GPTQ-8b	Meta-Llama-3-70B-Instruct-GPTQ
Avg.	72.5	72.7	71.3
ARC_es	66.7	66.9	65.7
Hellaswag_es	75.8	75.9	74
MMLU_es	75	75.3	74.2

We did not check for data contamination. Evaluation was done using Eval. Harness using limit=1000.

Performance

	requests/s	tokens/s
NVIDIA L40Sx2	2	951.28
Performance measured on cortecs inference.