Instructions to use aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K with Transformers:

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

pipe = pipeline("text-generation", model="aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K")
model = AutoModelForCausalLM.from_pretrained("aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K")

Inference
Local Apps Settings

vLLM

How to use aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K

SGLang

How to use aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K 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 "aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K with Docker Model Runner:
```
docker model run hf.co/aifeifei798/llama3-8B-DarkIdol-2.2-Uncensored-1048K
```

llama3-8B-DarkIdol-2.2-Uncensored-1048K / model-00004-of-00004.safetensors

aifeifei798

Upload model-00004-of-00004.safetensors

fbef190 verified almost 2 years ago

Download

History Blame

1.19 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 4b3ad7d3f281cc0d7fb5f7e5c85cde48cb22af5e9511dff80bf901b6d10f9afc
Size of remote file:: 1.19 GB
SHA256:: 3ca53de9ba3f7564d473d20e48cc8b79c705b373bf4be2b856617462156ed31a

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.