Instructions to use zohaib99k/WizardLM-7B-uncensored-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zohaib99k/WizardLM-7B-uncensored-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="zohaib99k/WizardLM-7B-uncensored-GPTQ")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("zohaib99k/WizardLM-7B-uncensored-GPTQ")
model = AutoModelForMultimodalLM.from_pretrained("zohaib99k/WizardLM-7B-uncensored-GPTQ")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zohaib99k/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 "zohaib99k/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": "zohaib99k/WizardLM-7B-uncensored-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zohaib99k/WizardLM-7B-uncensored-GPTQ

SGLang

How to use zohaib99k/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 "zohaib99k/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": "zohaib99k/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 "zohaib99k/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": "zohaib99k/WizardLM-7B-uncensored-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zohaib99k/WizardLM-7B-uncensored-GPTQ with Docker Model Runner:
```
docker model run hf.co/zohaib99k/WizardLM-7B-uncensored-GPTQ
```

WizardLM-7B-uncensored-GPTQ / README.md

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	---
	license: apache-2.0
	datasets:
	- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
	inference: false
	---

	# WizardLM - uncensored: An Instruction-following LLM Using Evol-Instruct

	These files are GPTQ 4bit model files for [Eric Hartford's 'uncensored' version of WizardLM](https://huggingface.co/ehartford/WizardLM-7B-Uncensored).

	It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).

	Eric did a fresh 7B training using the WizardLM method, on [a dataset edited to remove all the "I'm sorry.." type ChatGPT responses](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered).

	## Other repositories available

	* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ)
	* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML)
	* [Eric's unquantised model in HF format](https://huggingface.co/ehartford/WizardLM-7B-Uncensored)

	## How to easily download and use this model in text-generation-webui

	Open the text-generation-webui UI as normal.

	1. Click the Model tab.
	2. Under Download custom model or LoRA, enter `TheBloke/WizardLM-7B-uncensored-GPTQ`.
	3. Click Download.
	4. Wait until it says it's finished downloading.
	5. Click the Refresh icon next to Model in the top left.
	6. In the Model drop-down: choose the model you just downloaded, `WizardLM-7B-uncensored-GPTQ`.
	7. If you see an error in the bottom right, ignore it - it's temporary.
	8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama`
	9. Click Save settings for this model in the top right.
	10. Click Reload the Model in the top right.
	11. Once it says it's loaded, click the Text Generation tab and enter a prompt!

	## Provided files

	Compatible file - WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors

	In the `main` branch - the default one - you will find `WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors`

	This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility

	It was created without the `--act-order` parameter. It may have slightly lower inference quality compared to the other file, but is guaranteed to work on all versions of GPTQ-for-LLaMa and text-generation-webui.

	* `wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors`
	* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
	* Works with text-generation-webui one-click-installers
	* Parameters: Groupsize = 128g. No act-order.
	* Command used to create the GPTQ:
	```
	python llama.py models/ehartford_WizardLM-7B-Uncensored c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors /workspace/eric-gptq/WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors
	```

	# Eric's original model card

	This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.

	Shout out to the open source AI/ML community, and everyone who helped me out, including Rohan, TheBloke, and Caseus

	# WizardLM's original model card

	Overview of Evol-Instruct
	Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.

	![info](https://github.com/nlpxucan/WizardLM/raw/main/imgs/git_overall.png)
	![info](https://github.com/nlpxucan/WizardLM/raw/main/imgs/git_running.png)