Instructions to use cortecs/Meta-Llama-3-70B-Instruct-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cortecs/Meta-Llama-3-70B-Instruct-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="cortecs/Meta-Llama-3-70B-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-3-70B-Instruct-GPTQ")
model = AutoModelForCausalLM.from_pretrained("cortecs/Meta-Llama-3-70B-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-3-70B-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-3-70B-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-3-70B-Instruct-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortecs/Meta-Llama-3-70B-Instruct-GPTQ

SGLang

How to use cortecs/Meta-Llama-3-70B-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-3-70B-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-3-70B-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-3-70B-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-3-70B-Instruct-GPTQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cortecs/Meta-Llama-3-70B-Instruct-GPTQ with Docker Model Runner:
```
docker model run hf.co/cortecs/Meta-Llama-3-70B-Instruct-GPTQ
```

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

Downloads last month: 15

cortecs
/

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

Usage

Evaluations

Performance

Dataset used to train cortecs/Meta-Llama-3-70B-Instruct-GPTQ