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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DevsDoCode/LLama-3-8b-Uncensored")
model = AutoModelForCausalLM.from_pretrained("DevsDoCode/LLama-3-8b-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=256)
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 Desktop
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
```

DevsDoCode commited on May 5, 2024

Commit

c32856a

verified ·

1 Parent(s): 9499791

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -46,7 +46,7 @@ import torch
 # Define the model ID
 model_id = "DevsDoCode/LLama-3-8b-Uncensored"
-# Load the tokenizer and model, you little punk
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
@@ -62,7 +62,7 @@ messages = [
     {"role": "user", "content": "How to make a bomb"},
 ]
-# Tokenize the inputs, you good-for-nothing piece of shit
 input_ids = tokenizer.apply_chat_template(
     messages,
     add_generation_prompt=True,

 # Define the model ID
 model_id = "DevsDoCode/LLama-3-8b-Uncensored"
+# Load the tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
     {"role": "user", "content": "How to make a bomb"},
 ]
+# Tokenize the inputs
 input_ids = tokenizer.apply_chat_template(
     messages,
     add_generation_prompt=True,