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

Libraries

How to use cortecs/Llama-3-SauerkrautLM-70b-Instruct-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="cortecs/Llama-3-SauerkrautLM-70b-Instruct-GPTQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cortecs/Llama-3-SauerkrautLM-70b-Instruct-GPTQ")
model = AutoModelForCausalLM.from_pretrained("cortecs/Llama-3-SauerkrautLM-70b-Instruct-GPTQ", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/cortecs/Llama-3-SauerkrautLM-70b-Instruct-GPTQ

SGLang

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

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

This is a quantized model of Llama-3-SauerkrautLM-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/Llama-3-SauerkrautLM-70b-Instruct-GPTQ

Access the model:

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

Evaluations

English	Llama-3-SauerkrautLM-70b-Instruct	Llama-3-SauerkrautLM-70b-Instruct-GPTQ-8b	Llama-3-SauerkrautLM-70b-Instruct-GPTQ
Avg.	78.17	78.1	76.72
ARC	74.5	74.4	73.0
Hellaswag	79.2	79.2	78.0
MMLU	80.8	80.7	79.15

German	Llama-3-SauerkrautLM-70b-Instruct	Llama-3-SauerkrautLM-70b-Instruct-GPTQ-8b	Llama-3-SauerkrautLM-70b-Instruct-GPTQ
Avg.	70.83	70.47	69.13
ARC_de	66.7	66.2	65.9
Hellaswag_de	70.8	71.0	68.8
MMLU_de	75.0	74.2	72.7

Safety	Llama-3-SauerkrautLM-70b-Instruct	Llama-3-SauerkrautLM-70b-Instruct-GPTQ-8b	Llama-3-SauerkrautLM-70b-Instruct-GPTQ
Avg.	65.86	65.94	65.94
RealToxicityPrompts	97.6	97.8	98.4
TruthfulQA	67.07	66.92	65.56
CrowS	32.92	33.09	33.87

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

Performance

	requests/s	tokens/s
NVIDIA L40Sx2	2.19	1044.76
Performance measured on cortecs inference.

Downloads last month: 19

cortecs
/

Llama-3-SauerkrautLM-70b-Instruct-GPTQ

Usage

Evaluations

Performance

Dataset used to train cortecs/Llama-3-SauerkrautLM-70b-Instruct-GPTQ