Instructions to use TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF with Ollama:
```
ollama run hf.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF with Docker Model Runner:
```
docker model run hf.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M
```

Lemonade

How to use TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

TheBloke commited on Sep 20, 2023

Commit

88b01e1

1 Parent(s): 516052e

Upload README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -142,7 +142,7 @@ The following clients/libraries will automatically download models for you, prov
 ### In `text-generation-webui`
-Under Download Model, you can enter the model repo: TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF and below it, a specific filename to download, such as: WizardLM-Uncensored-SuperCOT-Storytelling.q4_K_M.gguf.
 Then click Download.
@@ -151,13 +151,13 @@ Then click Download.
 I recommend using the `huggingface-hub` Python library:
 ```shell
-pip3 install huggingface-hub>=0.17.1
 ```
 Then you can download any individual model file to the current directory, at high speed, with a command like this:
 ```shell
-huggingface-cli download TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF WizardLM-Uncensored-SuperCOT-Storytelling.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 <details>
@@ -180,7 +180,7 @@ pip3 install hf_transfer
 And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
 ```shell
-HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF WizardLM-Uncensored-SuperCOT-Storytelling.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
@@ -193,7 +193,7 @@ Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running
 Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 ```shell
-./main -ngl 32 -m WizardLM-Uncensored-SuperCOT-Storytelling.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful AI assistant.\n\nUSER: {prompt}\nASSISTANT:"
 ```
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
@@ -233,7 +233,7 @@ CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
 from ctransformers import AutoModelForCausalLM
 # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
-llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF", model_file="WizardLM-Uncensored-SuperCOT-Storytelling.q4_K_M.gguf", model_type="llama", gpu_layers=50)
 print(llm("AI is going to"))
 ```

 ### In `text-generation-webui`
+Under Download Model, you can enter the model repo: TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF and below it, a specific filename to download, such as: WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf.
 Then click Download.
 I recommend using the `huggingface-hub` Python library:
 ```shell
+pip3 install huggingface-hub
 ```
 Then you can download any individual model file to the current directory, at high speed, with a command like this:
 ```shell
+huggingface-cli download TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 <details>
 And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
 ```shell
+HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
 Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
 ```shell
+./main -ngl 32 -m WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful AI assistant.\n\nUSER: {prompt}\nASSISTANT:"
 ```
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
 from ctransformers import AutoModelForCausalLM
 # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
+llm = AutoModelForCausalLM.from_pretrained("TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF", model_file="WizardLM-Uncensored-SuperCOT-Storytelling.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
 print(llm("AI is going to"))
 ```