Instructions to use Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF",
	filename="Llama-3-8B-Instruct-DADA-iMat-IQ1_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-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 Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-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 Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF with Ollama:
```
ollama run hf.co/Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M
```

Unsloth Studio

How to use Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-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 Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-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 Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF to start chatting

Docker Model Runner
How to use Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M
```

Lemonade

How to use Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Quant-Cartel/Llama-3-8B-Instruct-DADA-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-8B-Instruct-DADA-iMat-GGUF-Q4_K_M

List all available models

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PROUDLY PRESENTS

Llama-3-8B-Instruct-DADA-iMat-GGUF

Quantized from fp16 with love.

Weighted quantizations were calculated using groups_merged.txt with 105 chunks (recommended amount for this file) and n_ctx=512. Special thanks to jukofyork for sharing this process

For a brief rundown of iMatrix quant performance please see this PR

All quants are verified working prior to uploading to repo for your safety and convenience.

Please note importance matrix quantizations are a work in progress. IQ4 and above is recommended for best results.

Original model card here and below:

Llama-3-8B-Instruct-DADA

Warning: This model is experimental and thus potentially unpredictable.

This model employs the same strategy as Mixtral Instruct ITR DADA

I trained Llama-3-8B-Instruct on the Alpaca-DADA dataset for 10 epochs at 1e-6 learning rate. I then did a 50/50 SLERP merge of the resulting model back onto Llama-3-8B-Instruct

This model may require custom stopping strings to tame due to current issues surrounding Llama-3 EOS tokens and various back-ends. It certainly gives some interesting answers using an assistant template/card in SillyTavern, though.

The below answer is one of the more interesting answers I've gotten out of an LLM on the same query, although there was an indentiation error (indicated by the red circle)

Training was done using qlora-pipe

Downloads last month: 277

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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