Instructions to use Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request 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/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request: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/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request: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/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request: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/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M

Use Docker

docker model run hf.co/Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M

LM Studio
Jan
Ollama
How to use Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request with Ollama:
```
ollama run hf.co/Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M
```

Unsloth Studio

How to use Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request 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/T.E-8.1-GGUF-IQ-Imatrix-Request 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/T.E-8.1-GGUF-IQ-Imatrix-Request to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request to start chatting

Docker Model Runner
How to use Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request with Docker Model Runner:
```
docker model run hf.co/Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M
```

Lemonade

How to use Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request:Q4_K_M

Run and chat with the model

lemonade run user.T.E-8.1-GGUF-IQ-Imatrix-Request-Q4_K_M

List all available models

lemonade list

Atomic Chat

Model name:
T.E-8.1

Brief description:
Qwen2.5-7B based model trained for roleplay uses.

Presets:
You can use the built in ChatML presets within SillyTavern and adjust from there.
Alternatively, check out Virt-io's ChatML v1.9 presets here, make sure you read the repository page for how to use them properly.

Request page:
https://huggingface.co/Lewdiculous/Model-Requests/discussions/76

Model link:
https://huggingface.co/Cran-May/T.E-8.1

Quantized with llama.cpp:
b3837
1. Base⇢ Convert-GGUF(FP16)⇢ Generate-Imatrix-Data(FP16)
2. Base⇢ Convert-GGUF(BF16)⇢ Use-Imatrix-Data(FP16)⇢ Quantize-GGUF(Imatrix-Quants)

Downloads last month: 30

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for Lewdiculous/T.E-8.1-GGUF-IQ-Imatrix-Request

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

Cran-May/T.E-8.1

Quantized

(6)

this model