Instructions to use azale-ai/DukunLM-7B-V1.0-Uncensored-sharded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use azale-ai/DukunLM-7B-V1.0-Uncensored-sharded with Transformers:

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

pipe = pipeline("text-generation", model="azale-ai/DukunLM-7B-V1.0-Uncensored-sharded")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored-sharded")
model = AutoModelForMultimodalLM.from_pretrained("azale-ai/DukunLM-7B-V1.0-Uncensored-sharded")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use azale-ai/DukunLM-7B-V1.0-Uncensored-sharded with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/azale-ai/DukunLM-7B-V1.0-Uncensored-sharded

SGLang

How to use azale-ai/DukunLM-7B-V1.0-Uncensored-sharded 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 "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded" \
    --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": "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded",
		"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 "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded" \
        --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": "azale-ai/DukunLM-7B-V1.0-Uncensored-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use azale-ai/DukunLM-7B-V1.0-Uncensored-sharded with Docker Model Runner:
```
docker model run hf.co/azale-ai/DukunLM-7B-V1.0-Uncensored-sharded
```

DukunLM-7B-V1.0-Uncensored-sharded / model-00009-of-00014.safetensors

hafidhsoekma

Adding `safetensors` variant of this model (#1)

3b02aa3 verified 11 months ago

Download

History Blame Contribute Delete

1.98 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:: d3a93b72c69bbb1aacd32338fb3b612a2b2ba74ec332734709d37b70461177d5
Size of remote file:: 1.98 GB
SHA256:: e1e254cfaed491a83d34bfee7085c6e7e6569457c880337cd1b21cd3cc8240af

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