Text Generation
GGUF
English
flatbuild
language-model
conversational
flatseek
flatrun
trained-from-scratch
rope
rmsnorm
swiglu
gqa
Instructions to use flatseek/flatbot-micro-4M 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 flatseek/flatbot-micro-4M 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 flatseek/flatbot-micro-4M # Run inference directly in the terminal: llama cli -hf flatseek/flatbot-micro-4M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf flatseek/flatbot-micro-4M # Run inference directly in the terminal: llama cli -hf flatseek/flatbot-micro-4M
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 flatseek/flatbot-micro-4M # Run inference directly in the terminal: ./llama-cli -hf flatseek/flatbot-micro-4M
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 flatseek/flatbot-micro-4M # Run inference directly in the terminal: ./build/bin/llama-cli -hf flatseek/flatbot-micro-4M
Use Docker
docker model run hf.co/flatseek/flatbot-micro-4M
- LM Studio
- Jan
- vLLM
How to use flatseek/flatbot-micro-4M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "flatseek/flatbot-micro-4M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flatseek/flatbot-micro-4M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/flatseek/flatbot-micro-4M
- Ollama
How to use flatseek/flatbot-micro-4M with Ollama:
ollama run hf.co/flatseek/flatbot-micro-4M
- Unsloth Studio
How to use flatseek/flatbot-micro-4M 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 flatseek/flatbot-micro-4M 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 flatseek/flatbot-micro-4M to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for flatseek/flatbot-micro-4M to start chatting
- Docker Model Runner
How to use flatseek/flatbot-micro-4M with Docker Model Runner:
docker model run hf.co/flatseek/flatbot-micro-4M
- Lemonade
How to use flatseek/flatbot-micro-4M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull flatseek/flatbot-micro-4M
Run and chat with the model
lemonade run user.flatbot-micro-4M-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Update README.md
Browse files
README.md
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pipeline_tag: text-generation
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---
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# Flatbot-4M
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Flatbot-4M is the flagship demonstration model for **Flatbuild**.
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It is a compact conversational language model trained entirely from scratch using the Flatbuild framework. The goal is not to compete with large language models, but to demonstrate the complete workflow of building a modern transformer—from dataset generation and tokenizer training to model training, checkpoint export, and inference.
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## Usage
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### Flatbuild
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```bash
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```
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### Python
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```python
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from flatbuild import AutoModel
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model = AutoModel.from_pretrained("flatseek/Flatbot-4M")
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print(
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model.generate(
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"Hello! What can you do?",
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chat=True,
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)
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```
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### Flatrun
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--prompt "Hello!"
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```
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## Purpose
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pipeline_tag: text-generation
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# Flatbot-micro-4M
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Flatbot-micro-4M is the flagship demonstration model for **Flatbuild**.
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It is a compact conversational language model trained entirely from scratch using the Flatbuild framework. The goal is not to compete with large language models, but to demonstrate the complete workflow of building a modern transformer—from dataset generation and tokenizer training to model training, checkpoint export, and inference.
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## Usage
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### Flatbuild Training
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```bash
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pip install flatbuild
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flatbuild train config/demo_chat.yml
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flatbuild export output/demo_chat/*/checkpoint/final --format gguf --output flatbot-micro-4M.gguf
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```
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### Flatrun
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```bash
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pip install flatbuild
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flatrun chat --model flatbot-micro-4M.gguf --prompt "Hello!"
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```
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## Purpose
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