Instructions to use RaspizdAI/eblan-1.5-flash-lite 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/eblan-1.5-flash-lite 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/eblan-1.5-flash-lite
# Run inference directly in the terminal:
llama cli -hf RaspizdAI/eblan-1.5-flash-lite

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RaspizdAI/eblan-1.5-flash-lite
# Run inference directly in the terminal:
llama cli -hf RaspizdAI/eblan-1.5-flash-lite

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/eblan-1.5-flash-lite
# Run inference directly in the terminal:
./llama-cli -hf RaspizdAI/eblan-1.5-flash-lite

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/eblan-1.5-flash-lite
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RaspizdAI/eblan-1.5-flash-lite

Use Docker

docker model run hf.co/RaspizdAI/eblan-1.5-flash-lite

LM Studio
Jan
Ollama
How to use RaspizdAI/eblan-1.5-flash-lite with Ollama:
```
ollama run hf.co/RaspizdAI/eblan-1.5-flash-lite
```

Unsloth Studio

How to use RaspizdAI/eblan-1.5-flash-lite 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/eblan-1.5-flash-lite 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/eblan-1.5-flash-lite to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use RaspizdAI/eblan-1.5-flash-lite with Docker Model Runner:
```
docker model run hf.co/RaspizdAI/eblan-1.5-flash-lite
```

Lemonade

How to use RaspizdAI/eblan-1.5-flash-lite with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RaspizdAI/eblan-1.5-flash-lite

Run and chat with the model

lemonade run user.eblan-1.5-flash-lite-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

🚀 eblan-1.5-flash-lite

The fastest lightweight model fine-tuned on pure entropy.

Technical Specifications:

Architecture: EblanForCasualLM
Total Parameters: 6,432,897 (~6.4M)
Trainable Parameters: 1 (specifically stupidity_param)
Vocabulary Size: ~50,257 (GPT-2 standard Byte-BPE)
Embedding Dimensions: 64
Hidden Layers: 1
Attention Heads: 1

Benchmark Results:

Benchmark	Score
AIME 2025	`0.0`
HLE	`0.0`
Terminal bench 2.1	`0.0`
SWE Bench verified	`0.0`

Loss curve

Downloads last month: 63

Safetensors

Model size

6.43M params

Tensor type

F32

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Collection including RaspizdAI/eblan-1.5-flash-lite

Eblan models

Collection

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