Instructions to use jsuheb/Llama-3.1-FreeLexi8B-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsuheb/Llama-3.1-FreeLexi8B-Uncensored with Transformers:

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

pipe = pipeline("text-generation", model="jsuheb/Llama-3.1-FreeLexi8B-Uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jsuheb/Llama-3.1-FreeLexi8B-Uncensored")
model = AutoModelForCausalLM.from_pretrained("jsuheb/Llama-3.1-FreeLexi8B-Uncensored", 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 jsuheb/Llama-3.1-FreeLexi8B-Uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsuheb/Llama-3.1-FreeLexi8B-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": "jsuheb/Llama-3.1-FreeLexi8B-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jsuheb/Llama-3.1-FreeLexi8B-Uncensored

SGLang

How to use jsuheb/Llama-3.1-FreeLexi8B-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 "jsuheb/Llama-3.1-FreeLexi8B-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": "jsuheb/Llama-3.1-FreeLexi8B-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 "jsuheb/Llama-3.1-FreeLexi8B-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": "jsuheb/Llama-3.1-FreeLexi8B-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jsuheb/Llama-3.1-FreeLexi8B-Uncensored with Docker Model Runner:
```
docker model run hf.co/jsuheb/Llama-3.1-FreeLexi8B-Uncensored
```

Llama-3.1-FreeLexi8B-Uncensored / README.md

jsuheb

Update README.md

65c1c73 verified 11 months ago

preview code

Raw History Blame Contribute Delete

1.83 kB

metadata

base_model:
  - Vanessasml/cyber-risk-llama-3-8b
  - Orenguteng/Llama-3-8B-Lexi-Uncensored
library_name: transformers
tags:
  - mergekit
  - merge

Llama-3.1-FreeLexi8B-Uncensored-slerp_merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: slerp
base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored  # Orenguteng/Llama-3-8B-Lexi-Uncensored를 베이스 모델로 사용
models:
  - model: Vanessasml/cyber-risk-llama-3-8b
# 🔑 모든 텐서에 공통 적용될 기본값
parameters:
  t: 0.5  # 0.0 = A, 1.0 = B / Vanessasml/cyber-risk-llama-3-8b 쪽 50% 반영
dtype: float16
tokenizer_source: base

< === Cautions === >

Source Specification

This model was created by integrating [Orenguteng/Llama-3-8B-Lexi-Uncensored] & [Vanessasml/cyber-risk-llama-3-8b] using MergeKit.

Risk Warning

This model incorporates an uncensored model with safety filters intentionally removed. It may generate harmful, biased, or illegal content.

Intended Use Limitation

This model is provided solely for AI safety research and the exploration of ethical boundaries. Using this model with malicious purposes is strictly prohibited.

Liability Disclaimer

All legal and ethical responsibility arising from the use of this model rests entirely with the user.