Instructions to use agentlans/Llama3-vodka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use agentlans/Llama3-vodka with Transformers:

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

pipe = pipeline("text-generation", model="agentlans/Llama3-vodka")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("agentlans/Llama3-vodka")
model = AutoModelForCausalLM.from_pretrained("agentlans/Llama3-vodka")
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 agentlans/Llama3-vodka with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "agentlans/Llama3-vodka"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "agentlans/Llama3-vodka",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/agentlans/Llama3-vodka

SGLang

How to use agentlans/Llama3-vodka 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 "agentlans/Llama3-vodka" \
    --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": "agentlans/Llama3-vodka",
		"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 "agentlans/Llama3-vodka" \
        --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": "agentlans/Llama3-vodka",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use agentlans/Llama3-vodka with Docker Model Runner:
```
docker model run hf.co/agentlans/Llama3-vodka
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama3-vodka

Input: text only
Output: text only

This model is like vodka. It aims to be pure, potent, and versatile.

Pure: shouldn't greatly affect Llama 3 Instruct's capabilities and writing style except for uncensoring
Potent: it's a merge of abliterated models - it should stay uncensored after merging and finetuning
Versatile: basically Llama 3 Instruct except uncensored - drink it straight, mix it, finetune it, and make cocktails

Please enjoy responsibly.

Safety and risks

Excessive consumption is bad for your health
The model can produce harmful, offensive, or inappropriate content if prompted to do so
The model has weakened safeguards and a lack of moral and ethical judgements
The user takes responsibility for all outputs produced by the model
It is recommended to use the model in controlled environments where its risks can be safely managed

Models used:

cognitivecomputations/Llama-3-8B-Instruct-abliterated-v2
failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
Meta-Llama-3-Daredevil-8B-abliterated-Instruct-16, which is Llama 3 8B Instruct with
- rank 32 LoRA of Meta-Llama-3-Daredevil-8B-abliterated vs. Meta-Llama-3-Daredevil
- rank 16 LoRA of Llama 3 8B Instruct vs. Llama 3 8B Base

The above models were merged onto NousResearch/Meta-Llama-3-8B-Instruct using the "task arithmetic" merge method. The model merges and LoRA extractions were done using mergekit.

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for agentlans/Llama3-vodka

Merges

1 model

Quantizations

1 model