Instructions to use OwenArli/ArliAI-Llama-3-8B-Dolfin-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-Dolfin-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-Dolfin-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OwenArli/ArliAI-Llama-3-8B-Dolfin-v1.0")
model = AutoModelForCausalLM.from_pretrained("OwenArli/ArliAI-Llama-3-8B-Dolfin-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 OwenArli/ArliAI-Llama-3-8B-Dolfin-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-Dolfin-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-Dolfin-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use OwenArli/ArliAI-Llama-3-8B-Dolfin-v1.0 with Docker Model Runner:
```
docker model run hf.co/OwenArli/ArliAI-Llama-3-8B-Dolfin-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

Base model: https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3

SFT fine tune of Meta Llama 3 8B Instruct Abliterated v3 by Failspy using an improved Dolphin and WizardLM dataset intended to remove GPT-isms and make the model follow instructions more exactly while paying attention to details better.

Since it is based on the Abliterated version of Llama 3 8B Instruct it should naturally not refuse to answer in the first place and this fine tuning should make it comply even better.

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 2.5 days on an RTX 4090
1 epoch training with a massive dataset for minimized repetition sickness.
Using 4-bit loading and Qlora 64-rank 64-alpha resulting in ~2% trainable weights.

Llama 3 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-Dolfin-v1.0

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

Downloads last month: 18

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for OwenArli/ArliAI-Llama-3-8B-Dolfin-v1.0

Quantizations

5 models