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
- llama.cpp
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
File size: 746 Bytes
92788db a104a01 92788db c67d388 8e60591 c67d388 8e60591 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | ---
license: mit
tags:
- eblan
---
# 🚀 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
 |