Instructions to use 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter with Transformers:

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

pipe = pipeline("text-generation", model="0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter")
model = AutoModelForCausalLM.from_pretrained("0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter", 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 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter

SGLang

How to use 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter 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 "0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter" \
    --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": "0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter",
		"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 "0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter" \
        --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": "0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter",
    max_seq_length=2048,
)

Docker Model Runner
How to use 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter with Docker Model Runner:
```
docker model run hf.co/0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3.3-8B-Instruct-OmniWriter

An experiment in creating a creative, uncensored storyteller based on the "smart" Llama 3.3 8B Instruct base.

Training Specs

Training Method: LoRA (16-bit)
LoRA Rank (r): 32
LoRA Alpha: 16
Scaling Type: Rank-Stabilized LoRA (RS-LoRA)
Batch Size: 32
Gradient Accumulation: 2
Epochs: 1
Learning Rate: 2e-5
Optimizer: AdamW (Fused)
LR Scheduler: Cosine
Noise Level: NEFTune (alpha=5)

Special Thanks

This fine-tune wouldn't be possible without the incredible work of the community:

p-e-w for developing Heretic - an essential tool for censorship removal.
allura-forge and shb777 for providing access to the Llama 3.3 8B weights.
Gryphe for the meticulously curated Writing Prompts datasets (Opus and ChatGPT-4o versions).
unsloth for their training acceleration framework that makes local fine-tuning fast and memory-efficient.
AMD for their Instinct™ MI300X GPU.

Downloads last month: 81

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter

Quantizations

2 models

0xA50C1A1
/

Llama-3.3-8B-Instruct-OmniWriter

Llama-3.3-8B-Instruct-OmniWriter

Training Specs

Special Thanks

Model tree for 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter

Datasets used to train 0xA50C1A1/Llama-3.3-8B-Instruct-OmniWriter