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

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	---
	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](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.

	### Models Merged

	The following models were included in the merge:
	* [Vanessasml/cyber-risk-llama-3-8b](https://huggingface.co/Vanessasml/cyber-risk-llama-3-8b)
	* [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	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.