Instructions to use seonglae/wizardlm-7b-uncensored-gptq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use seonglae/wizardlm-7b-uncensored-gptq with Transformers:

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

pipe = pipeline("text-generation", model="seonglae/wizardlm-7b-uncensored-gptq")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("seonglae/wizardlm-7b-uncensored-gptq")
model = AutoModelForMultimodalLM.from_pretrained("seonglae/wizardlm-7b-uncensored-gptq")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use seonglae/wizardlm-7b-uncensored-gptq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "seonglae/wizardlm-7b-uncensored-gptq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "seonglae/wizardlm-7b-uncensored-gptq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/seonglae/wizardlm-7b-uncensored-gptq

SGLang

How to use seonglae/wizardlm-7b-uncensored-gptq 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 "seonglae/wizardlm-7b-uncensored-gptq" \
    --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": "seonglae/wizardlm-7b-uncensored-gptq",
		"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 "seonglae/wizardlm-7b-uncensored-gptq" \
        --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": "seonglae/wizardlm-7b-uncensored-gptq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use seonglae/wizardlm-7b-uncensored-gptq with Docker Model Runner:
```
docker model run hf.co/seonglae/wizardlm-7b-uncensored-gptq
```

Get Started

This model should use AutoGPTQ so you need to use auto-gptq

no-act-order model
4bit model quantization

from transformers import AutoTokenizer, pipeline, AutoModelForCausalLM, LlamaForCausalLM, LlamaTokenizer, StoppingCriteria, PreTrainedTokenizerBase
from auto_gptq import AutoGPTQForCausalLM

model_id = 'seonglae/wizardlm-7b-uncensored-gptq'
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(
        model_id,
        model_basename=model_basename,
        trust_remote_code=True,
        device='cuda:0',
        use_triton=False,
        use_safetensors=True,
)

pipe = pipeline(
      "text-generation",
      model=model,
      tokenizer=tokenizer,
      temperature=0.5,
      top_p=0.95,
      max_new_tokens=100,
      repetition_penalty=1.15,
)
prompt = "USER: Are you AI?\nASSISTANT:"
pipe(prompt)

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wizardlm-7b-uncensored-gptq

Get Started

Dataset used to train seonglae/wizardlm-7b-uncensored-gptq