Instructions to use nold/WSB-GPT-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nold/WSB-GPT-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nold/WSB-GPT-7B-GGUF",
	filename="WSB-GPT-7B_Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nold/WSB-GPT-7B-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 nold/WSB-GPT-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nold/WSB-GPT-7B-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 nold/WSB-GPT-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nold/WSB-GPT-7B-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 nold/WSB-GPT-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nold/WSB-GPT-7B-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 nold/WSB-GPT-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nold/WSB-GPT-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/nold/WSB-GPT-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use nold/WSB-GPT-7B-GGUF with Ollama:
```
ollama run hf.co/nold/WSB-GPT-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use nold/WSB-GPT-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nold/WSB-GPT-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nold/WSB-GPT-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nold/WSB-GPT-7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use nold/WSB-GPT-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/nold/WSB-GPT-7B-GGUF:Q4_K_M
```

Lemonade

How to use nold/WSB-GPT-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nold/WSB-GPT-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WSB-GPT-7B-GGUF-Q4_K_M

List all available models

lemonade list

Model Card for WSB-GPT-7B

This is a Llama 2 7B Chat model fine-tuned with QLoRA on 2017-2018ish /r/wallstreetbets subreddit comments and responses, with the hopes of learning more about QLoRA and creating models with a little more character.

Model Description

Developed by: Sentdex
Shared by: Sentdex
GPU Compute provided by: Lambda Labs
Model type: Instruct/Chat
Language(s) (NLP): Multilingual from Llama 2, but not sure what the fine-tune did to it, or if the fine-tuned behavior translates well to other languages. Let me know!
License: Apache 2.0
Finetuned from Llama 2 7B Chat
Demo [optional]: [More Information Needed]

Uses

This model's primary purpose is to be a fun chatbot and to learn more about QLoRA. It is not intended to be used for any other purpose and some people may find it abrasive/offensive.

Bias, Risks, and Limitations

This model is prone to using at least 3 words that were popularly used in the WSB subreddit in that era that are much more frowned-upon. As time goes on, I may wind up pruning or find-replacing these words in the training data, or leaving it.

Just be advised this model can be offensive and is not intended for all audiences!

How to Get Started with the Model

Prompt Format:

### Comment:
[parent comment text]

### REPLY:
[bot's reply]

### END.

Use the code below to get started with the model.

from transformers import pipeline

# Initialize the pipeline for text generation using the Sentdex/WSB-GPT-7B model
pipe = pipeline("text-generation", model="Sentdex/WSB-GPT-7B")

# Define your prompt
prompt = """### Comment:
How does the stock market actually work?

### REPLY:
"""

# Generate text based on the prompt
generated_text = pipe(prompt, max_length=128, num_return_sequences=1)

# Extract and print the generated text
print(generated_text[0]['generated_text'].split("### END.")[0])

Example continued generation from above:

### Comment:
How does the stock market actually work?

### REPLY:
You sell when you are up and buy when you are down.

Despite </s> being the typical Llama stop token, I was never able to get this token to be generated in training/testing so the model would just never stop generating. I wound up testing with ### END. and that worked, but obviously isn't ideal. Will fix this in the future maybe(tm).

Hardware

This QLoRA was trained on a Lambda Labs 1x H100 80GB GPU instance.

Citation

Llama 2 (Meta AI) for the base model.
Farouk E / Far El: https://twitter.com/far__el for helping with all my silly questions about QLoRA
Lambda Labs for the compute. The model itself only took a few hours to train, but it took me days to learn how to tie everything together.
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer for QLoRA + implementation on github: https://github.com/artidoro/qlora/
@eugene-yh and @jinyongyoo on Github + @ChrisHayduk for the QLoRA merge: https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930

Model Card Contact

harrison@pythonprogramming.net

Vanilla Quantization by nold, Model by WSB-GPT-7B

Downloads last month: 99

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

4-bit

5-bit

8-bit

Inference Providers NEW

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WSB-GPT-7B-GGUF