Instructions to use 44dummies/ted-llama3-8b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 44dummies/ted-llama3-8b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="44dummies/ted-llama3-8b-gguf",
	filename="llama3-base.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use 44dummies/ted-llama3-8b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 44dummies/ted-llama3-8b-gguf
# Run inference directly in the terminal:
llama-cli -hf 44dummies/ted-llama3-8b-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf 44dummies/ted-llama3-8b-gguf
# Run inference directly in the terminal:
llama-cli -hf 44dummies/ted-llama3-8b-gguf

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 44dummies/ted-llama3-8b-gguf
# Run inference directly in the terminal:
./llama-cli -hf 44dummies/ted-llama3-8b-gguf

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 44dummies/ted-llama3-8b-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 44dummies/ted-llama3-8b-gguf

Use Docker

docker model run hf.co/44dummies/ted-llama3-8b-gguf

LM Studio
Jan
Ollama
How to use 44dummies/ted-llama3-8b-gguf with Ollama:
```
ollama run hf.co/44dummies/ted-llama3-8b-gguf
```

Unsloth Studio new

How to use 44dummies/ted-llama3-8b-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 44dummies/ted-llama3-8b-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 44dummies/ted-llama3-8b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 44dummies/ted-llama3-8b-gguf to start chatting

Docker Model Runner
How to use 44dummies/ted-llama3-8b-gguf with Docker Model Runner:
```
docker model run hf.co/44dummies/ted-llama3-8b-gguf
```

Lemonade

How to use 44dummies/ted-llama3-8b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 44dummies/ted-llama3-8b-gguf

Run and chat with the model

lemonade run user.ted-llama3-8b-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Ted AI - Llama 3 8B Fine-tuned

No-bullshit AI assistant with quantitative trading expertise and tool-calling capabilities.

Files

File	Description	Size
`llama3-base.gguf`	Llama 3 8B Q4_K_M base model	~4.6 GB
`ted-lora.gguf`	Ted LoRA adapter	~161 MB
`Modelfile.local`	Ollama Modelfile	-

Usage with Ollama

# Download files
wget https://huggingface.co/44dummies/ted-llama3-8b-gguf/resolve/main/llama3-base.gguf
wget https://huggingface.co/44dummies/ted-llama3-8b-gguf/resolve/main/ted-lora.gguf
wget https://huggingface.co/44dummies/ted-llama3-8b-gguf/resolve/main/Modelfile.local

# Create model
ollama create ted -f Modelfile.local

# Run
ollama run ted

Training Details

Base Model: unsloth/llama-3-8b-bnb-4bit
Method: LoRA (r=16, alpha=16)
Training: 100 steps on custom dataset
Focus: Direct personality, trading knowledge, tool-calling

Personality

Ted is direct, uses dark humor, skips disclaimers, and actually solves problems. Specializes in quantitative trading, risk management, and systematic approaches.

Downloads last month: 3

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for 44dummies/ted-llama3-8b-gguf

Base model

meta-llama/Meta-Llama-3-8B

Quantized

(276)

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