Instructions to use cortecs/Meta-Llama-3-8B-Instruct-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortecs/Meta-Llama-3-8B-Instruct-GPTQ with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cortecs/Meta-Llama-3-8B-Instruct-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cortecs/Meta-Llama-3-8B-Instruct-GPTQ")
model = AutoModelForCausalLM.from_pretrained("cortecs/Meta-Llama-3-8B-Instruct-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cortecs/Meta-Llama-3-8B-Instruct-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortecs/Meta-Llama-3-8B-Instruct-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortecs/Meta-Llama-3-8B-Instruct-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ

SGLang

How to use cortecs/Meta-Llama-3-8B-Instruct-GPTQ with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cortecs/Meta-Llama-3-8B-Instruct-GPTQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortecs/Meta-Llama-3-8B-Instruct-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cortecs/Meta-Llama-3-8B-Instruct-GPTQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortecs/Meta-Llama-3-8B-Instruct-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cortecs/Meta-Llama-3-8B-Instruct-GPTQ with Docker Model Runner:
```
docker model run hf.co/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.

Downloads last month: 2

cortecs
/

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

Usage

Evaluations

Performance

Dataset used to train cortecs/Meta-Llama-3-8B-Instruct-GPTQ