Instructions to use TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ

SGLang

How to use TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-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 "TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-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": "TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-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 "TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-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": "TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ
```

Oobabooga Chat Errors

by PieDev - opened Sep 18, 2023

Discussion

PieDev

Sep 18, 2023

Oobabooga seems to be unable to run the model properly as shown below:

IronicKeyboard

Sep 23, 2023

•

edited Sep 23, 2023

I'm getting the same issue as of this post. Been playing around with different settings, but haven't been able to improve things much.

Been running this on runpod with the latest image, on a single L40

Edit note:
Solved the problem by ensure these instructions were properly followed:
To use the increased context, set the Loader to ExLlama, set max_seq_len to 8192 or 4096, and set compress_pos_emb to 4 for 8192 context, or to 2 for 4096 context.

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