Instructions to use OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0")
model = AutoModelForMultimodalLM.from_pretrained("OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0")
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 OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OwenArli/ArliAI-Llama-3-8B-Cumulus-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": "OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0

SGLang

How to use OwenArli/ArliAI-Llama-3-8B-Cumulus-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 "OwenArli/ArliAI-Llama-3-8B-Cumulus-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": "OwenArli/ArliAI-Llama-3-8B-Cumulus-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 "OwenArli/ArliAI-Llama-3-8B-Cumulus-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": "OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0 with Docker Model Runner:
```
docker model run hf.co/OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct

This is release v1.0 of Awanllm Cumulus series of models that aim to be uncensored and have zero refusals and zero warnings.

This model should be good for general use cases as the OG Llama 3 8B model but it should be especially better for story writing or RP use cases.

It is the most uncensored yet, thanks to using https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 as the base model.

In terms of reasoning and intelligence, this model is probably a bit worse than the OG Meta Llama 3 8B Instruct because of the decensoring. However we believe it is worth it for the decensoring, as even with jailbreak prompts Llama 3 8B Instruct will never get remotely close to this model.

Best practices:

Be precise and explain what you want the model to do. It has less base "personality" than the OG model but it will act however you tell it to.
This model works best with system prompts that tells it that it is the character, instead of telling it to act as a character.

Training:

Full 8192 sequence length.
Training duration is around 4 days on an RTX 4090, using 4-bit loading and Qlora 64-rank 64-alpha resulting in ~2% trainable weights.

Instruct format:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>

{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Quants:

FP16: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0

GGUF: https://huggingface.co/OwenArli/ArliAI-Llama-3-8B-Cumulus-v1.0-GGUF

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Safetensors

Model size

8B params

Tensor type

BF16

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Merges

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