Instructions to use Applied-Innovation-Center/Karnak-40B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Applied-Innovation-Center/Karnak-40B-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="Applied-Innovation-Center/Karnak-40B-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Applied-Innovation-Center/Karnak-40B-v1.0")
model = AutoModelForCausalLM.from_pretrained("Applied-Innovation-Center/Karnak-40B-v1.0", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Applied-Innovation-Center/Karnak-40B-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Applied-Innovation-Center/Karnak-40B-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Applied-Innovation-Center/Karnak-40B-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Applied-Innovation-Center/Karnak-40B-v1.0

SGLang

How to use Applied-Innovation-Center/Karnak-40B-v1.0 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 "Applied-Innovation-Center/Karnak-40B-v1.0" \
    --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": "Applied-Innovation-Center/Karnak-40B-v1.0",
		"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 "Applied-Innovation-Center/Karnak-40B-v1.0" \
        --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": "Applied-Innovation-Center/Karnak-40B-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Applied-Innovation-Center/Karnak-40B-v1.0 with Docker Model Runner:
```
docker model run hf.co/Applied-Innovation-Center/Karnak-40B-v1.0
```

AIC-MCIT commited on Apr 24

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@@ -22,6 +22,7 @@ inference: false
 ---
 # Karnak: Enhanced Arabic–English Large Language Model
 ## Model Summary

 ---
 # Karnak: Enhanced Arabic–English Large Language Model
+Karnak is a powerful AI model that works in both Arabic and English, with extra improvements that make it especially strong in Arabic and more natural in the way it writes and responds. It was built by taking an existing model and improving it through more training, so it can understand instructions better, handle longer text, and give more reliable answers. This makes it useful for everyday tasks like answering questions, explaining topics, writing content, or helping with work and research. It can also process long pieces of text, which is helpful for documents and extended conversations. A big advantage is that it is not locked to an online service only, since you can download it, run it locally on your own machine or servers, and even fine-tune it for your own specific use case.
 ## Model Summary