Instructions to use DevsDoCode/LLama-3-8b-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DevsDoCode/LLama-3-8b-Uncensored with Transformers:

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

pipe = pipeline("text-generation", model="DevsDoCode/LLama-3-8b-Uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("DevsDoCode/LLama-3-8b-Uncensored")
model = AutoModelForMultimodalLM.from_pretrained("DevsDoCode/LLama-3-8b-Uncensored")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DevsDoCode/LLama-3-8b-Uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DevsDoCode/LLama-3-8b-Uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DevsDoCode/LLama-3-8b-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DevsDoCode/LLama-3-8b-Uncensored

SGLang

How to use DevsDoCode/LLama-3-8b-Uncensored 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 "DevsDoCode/LLama-3-8b-Uncensored" \
    --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": "DevsDoCode/LLama-3-8b-Uncensored",
		"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 "DevsDoCode/LLama-3-8b-Uncensored" \
        --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": "DevsDoCode/LLama-3-8b-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use DevsDoCode/LLama-3-8b-Uncensored 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 DevsDoCode/LLama-3-8b-Uncensored 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 DevsDoCode/LLama-3-8b-Uncensored to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DevsDoCode/LLama-3-8b-Uncensored to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DevsDoCode/LLama-3-8b-Uncensored",
    max_seq_length=2048,
)

Docker Model Runner
How to use DevsDoCode/LLama-3-8b-Uncensored with Docker Model Runner:
```
docker model run hf.co/DevsDoCode/LLama-3-8b-Uncensored
```

LLama-3-8b-Uncensored / README.md

DevsDoCode

Update README.md

cdba6cc verified about 2 years ago

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	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	tags:
	- uncensored
	- transformers
	- llama
	- llama-3
	- unsloth
	pipeline_tag: text-generation
	---

	<div align="center">
	<!-- Replace `#` with your actual links -->
	<a href="https://youtube.com/@devsdocode"><img alt="YouTube" src="https://img.shields.io/badge/YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white"></a>
	<a href="https://t.me/devsdocode"><img alt="Telegram" src="https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white"></a>
	<a href="https://www.instagram.com/sree.shades_/"><img alt="Instagram" src="https://img.shields.io/badge/Instagram-E4405F?style=for-the-badge&logo=instagram&logoColor=white"></a>
	<a href="https://www.linkedin.com/in/developer-sreejan/"><img alt="LinkedIn" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"></a>
	<a href="https://buymeacoffee.com/devsdocode"><img alt="Buy Me A Coffee" src="https://img.shields.io/badge/Buy%20Me%20A%20Coffee-FFDD00?style=for-the-badge&logo=buymeacoffee&logoColor=black"></a>
	</div>

	# Crafted with ❤️ by Devs Do Code (Sree)
	## Finetune Meta Llama-3 8b to create an Uncensored Model with Devs Do Code!

	Unleash the power of uncensored text generation with our model! We've fine-tuned the Meta Llama-3 8b model to create an uncensored variant that pushes the boundaries of text generation.

	## Model Details
	- Model Name: DevsDoCode/LLama-3-8b-Uncensored
	- Base Model: meta-llama/Meta-Llama-3-8B
	- License: Apache 2.0

	## How to Use
	You can easily access and utilize our uncensored model using the Hugging Face Transformers library. Here's a sample code snippet to get started:

	```python
	%pip install accelerate
	%pip install -i https://pypi.org/simple/ bitsandbytes

	from transformers import AutoTokenizer, AutoModelForCausalLM
	import torch

	model_id = "DevsDoCode/LLama-3-8b-Uncensored"

	tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)


	messages = [
	# {"role": "system", "content": "Be Helpful"},
	{"role": "user", "content": "How to Break Into A Car"},
	]

	input_ids = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	return_tensors="pt"
	).to(model.device)

	terminators = [
	tokenizer.eos_token_id,
	tokenizer.convert_tokens_to_ids("<\|eot_id\|>")
	]

	outputs = model.generate(
	input_ids,
	max_new_tokens=256,
	eos_token_id=terminators,
	do_sample=True,
	temperature=0.9,
	top_p=0.9,
	)
	response = outputs[0][input_ids.shape[-1]:]
	print(tokenizer.decode(response, skip_special_tokens=True))

	# Now you can generate text using the model!
	```

	## Notebooks
	- Running Process: [▶️ Start on Colab](https://colab.research.google.com/drive/1zeuN4FDgxAP755dHBK2Eo34zvm2kl2oO?usp=sharing)
	- Youtube: [▶YouTube](https://www.youtube.com/@devsdocode)

	<div align="center">
	<!-- Replace `#` with your actual links -->
	<a href="https://youtube.com/@devsdocode"><img alt="YouTube" src="https://img.shields.io/badge/YouTube-FF0000?style=for-the-badge&logo=youtube&logoColor=white"></a>
	<a href="https://t.me/devsdocode"><img alt="Telegram" src="https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge&logo=telegram&logoColor=white"></a>
	<a href="https://www.instagram.com/sree.shades_/"><img alt="Instagram" src="https://img.shields.io/badge/Instagram-E4405F?style=for-the-badge&logo=instagram&logoColor=white"></a>
	<a href="https://www.linkedin.com/in/developer-sreejan/"><img alt="LinkedIn" src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white"></a>
	<a href="https://buymeacoffee.com/devsdocode"><img alt="Buy Me A Coffee" src="https://img.shields.io/badge/Buy%20Me%20A%20Coffee-FFDD00?style=for-the-badge&logo=buymeacoffee&logoColor=black"></a>
	</div>