Instructions to use TheBloke/WizardLM-Uncensored-Falcon-40B-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/WizardLM-Uncensored-Falcon-40B-GPTQ with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/WizardLM-Uncensored-Falcon-40B-GPTQ", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("TheBloke/WizardLM-Uncensored-Falcon-40B-GPTQ", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TheBloke/WizardLM-Uncensored-Falcon-40B-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/WizardLM-Uncensored-Falcon-40B-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-Uncensored-Falcon-40B-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/WizardLM-Uncensored-Falcon-40B-GPTQ

SGLang

How to use TheBloke/WizardLM-Uncensored-Falcon-40B-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-Uncensored-Falcon-40B-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-Uncensored-Falcon-40B-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-Uncensored-Falcon-40B-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-Uncensored-Falcon-40B-GPTQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TheBloke/WizardLM-Uncensored-Falcon-40B-GPTQ with Docker Model Runner:
```
docker model run hf.co/TheBloke/WizardLM-Uncensored-Falcon-40B-GPTQ
```

TheBloke commited on Jun 3, 2023

Commit

a91e6ff

1 Parent(s): 801c7f0

Update README.md

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README.md +7 -4

README.md CHANGED Viewed

@@ -36,27 +36,30 @@ It is also expected to be **VERY SLOW**. This is unavoidable at the moment, but
 To use it you will require:
-1. AutoGPTQ, from the latest `main` branch and compiled with `pip install .`
 2. `pip install einops`
 You can then use it immediately from Python code - see example code below - or from text-generation-webui.
 ## AutoGPTQ
-To install AutoGPTQ please follow these instructions:
 ```
 git clone https://github.com/PanQiWei/AutoGPTQ
 cd AutoGPTQ
 pip install .
 ```
-These steps will require that you have the [Nvidia CUDA toolkit](https://developer.nvidia.com/cuda-12-0-1-download-archive) installed.
 ## text-generation-webui
 There is also provisional AutoGPTQ support in text-generation-webui.
-This requires text-generation-webui as of commit 204731952ae59d79ea3805a425c73dd171d943c3.
 So please first update text-genration-webui to the latest version.

 To use it you will require:
+1. AutoGPTQ
 2. `pip install einops`
 You can then use it immediately from Python code - see example code below - or from text-generation-webui.
 ## AutoGPTQ
+Please install AutoGPTQ version 0.2.1 or later: `pip install auto-gptq`
+If you have any problems installing AutoGPTQ with CUDA support, you can try compiling manually from source:
 ```
 git clone https://github.com/PanQiWei/AutoGPTQ
 cd AutoGPTQ
 pip install .
 ```
+The manual installation steps will require that you have the [Nvidia CUDA toolkit](https://developer.nvidia.com/cuda-12-0-1-download-archive) installed.
 ## text-generation-webui
 There is also provisional AutoGPTQ support in text-generation-webui.
+This requires a text-generation-webui as of commit 204731952ae59d79ea3805a425c73dd171d943c3.
 So please first update text-genration-webui to the latest version.