Instructions to use RaspizdAI/debil-1.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RaspizdAI/debil-1.5-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 RaspizdAI/debil-1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RaspizdAI/debil-1.5-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 RaspizdAI/debil-1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RaspizdAI/debil-1.5-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 RaspizdAI/debil-1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RaspizdAI/debil-1.5-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 RaspizdAI/debil-1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RaspizdAI/debil-1.5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/RaspizdAI/debil-1.5-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use RaspizdAI/debil-1.5-GGUF with Ollama:
```
ollama run hf.co/RaspizdAI/debil-1.5-GGUF:Q4_K_M
```

Unsloth Studio

How to use RaspizdAI/debil-1.5-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 RaspizdAI/debil-1.5-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 RaspizdAI/debil-1.5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RaspizdAI/debil-1.5-GGUF to start chatting

Docker Model Runner
How to use RaspizdAI/debil-1.5-GGUF with Docker Model Runner:
```
docker model run hf.co/RaspizdAI/debil-1.5-GGUF:Q4_K_M
```

Lemonade

How to use RaspizdAI/debil-1.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RaspizdAI/debil-1.5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.debil-1.5-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

🚀 debil-1.5 GGUF

debil-1.5 in GGUF format, available in multiple quantization levels.

Technical Specifications:

Total Parameters: 46,538,400 (~46.5M)
Vocabulary Size: 50,257
Embedding Dimensions: 480
Hidden Layers: 8
Attention Heads: 8
Head Dimension: 60
Format: GGUF

Available Quantizations:

Quantization	File
FP16	`debil-1.5-fp16.gguf`
Q8_0	`debil-1.5-Q8_0.gguf`
Q4_K_M	`debil-1.5-Q4_K_M.gguf`
IQ2_XXS	`debil-1.5-IQ2_XXS.gguf`
IQ1_S	`debil-1.5-IQ1_S.gguf`

Quantization Details:

FP16 — unquantized version with FP16 weights.
Q8_0 — 8-bit quantization with minimal quality loss.
Q4_K_M — 4-bit K-quants offering a good size/quality balance.
IQ2_XXS — ultra-low-bit importance quantization.
IQ1_S — extremely aggressive 1-bit-class quantization for minimal file size.

Downloads last month: -

GGUF

Model size

46.5M params

Architecture

gpt2

Hardware compatibility

1-bit

2-bit

4-bit

8-bit

View +1 variant

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Collection including RaspizdAI/debil-1.5-GGUF

Debil 1.5

Collection

3 items • Updated about 3 hours ago