Instructions to use Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix",
	filename="Test2_SLIDE-F16.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 Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix 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 Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix: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 Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix: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 Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M

Use Docker

docker model run hf.co/Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M

LM Studio
Jan
Ollama
How to use Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix with Ollama:
```
ollama run hf.co/Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M
```

Unsloth Studio

How to use Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix 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 Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix 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 Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix to start chatting

Atomic Chat new
Docker Model Runner
How to use Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix with Docker Model Runner:
```
docker model run hf.co/Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M
```

Lemonade

How to use Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lewdiculous/Test2_SLIDE-GGUF-IQ-Imatrix:Q4_K_M

Run and chat with the model

lemonade run user.Test2_SLIDE-GGUF-IQ-Imatrix-Q4_K_M

List all available models

lemonade list

GGUF-IQ-Imatrix quants for NLPark/Test1_SLIDE as requested in #28.

Recommended presets here or here.
Use the latest version of KoboldCpp. Use the provided presets.
This is all still highly experimental, modified configs were used to avoid the tokenizer issues.

"Due to the poor performance of Test0 in Asian Languages, we trained a new preview model."

"It's a merge of https://huggingface.co/NLPark/Test1_SLIDE , https://huggingface.co/vicgalle/Configurable-Llama-3-8B-v0.3"

"The chat template of our chat models is similar as Llama3."

Downloads last month: 31

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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