The official chat interface for Flatbot-micro-4M, a tiny language model trained from scratch.
Flatbot is the official playground for Flatbot-micro-4M, a compact conversational language model built entirely from scratch using the Flatseek ecosystem.
This repository bundles the model, a modern ChatGPT-style web interface, and OpenAI-compatible APIs into a single application, making it easy to explore, deploy, and experiment with one of the smallest end-to-end language models available.
Unlike projects focused solely on inference, Flatbot showcases the complete lifecycle of an LLM—from tokenizer training and transformer training to GGUF export, streaming inference, and browser-based interaction.
Try it online:https://chat.flatseek.io
Download the model:https://huggingface.co/flatseek/flatbot-micro-4M
Flatbot-micro-4M is the flagship demonstration model of the Flatseek ecosystem.
Rather than competing with large foundation models, it demonstrates how a complete conversational AI model can be built from scratch using open-source tooling. The model is intentionally compact, making it suitable for education, experimentation, edge deployments, and understanding how modern LLMs work internally.
Experimental model: Flatbot is intended for learning and experimentation. Expect occasional hallucinations, factual inaccuracies, and inconsistent responses.
| Property | Value |
|---|---|
| Parameters | ~4.8 Million |
| Size | ~19 MB |
| Format | GGUF (F16) |
| Architecture | Decoder-only Transformer |
| Context Length | 512 tokens |
| Runtime | Flatrun |
| Training | From random initialization |
| Component | Details |
|---|---|
| Position Encoding | RoPE |
| Normalization | RMSNorm |
| Feed Forward | SwiGLU |
| Attention | Grouped Query Attention (GQA) |
| Weight Tying | Yes |
The model was trained entirely from random initialization using FlatBuild.
| Property | Value |
|---|---|
| Conversations | ~2,500 |
| Training Split | 95% |
| Validation Split | 5% |
| Dataset | General conversational dialogue |
Flatbot includes a lightweight browser-based interface for interacting with Flatbot-micro-4M.
- ChatGPT-style interface
- Real-time streaming responses
- Bundled Flatbot-micro-4M model
- CPU-only inference
- No external inference server
- OpenAI-compatible API
- Anthropic-compatible API
- Deployable on Vercel
Flatbot exposes APIs compatible with popular LLM clients.
POST /v1/chat/completions
POST /v1/messages
GET /v1/models
GET /healthz
curl https://chat.flatseek.io/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{ "model":"flatbot", "messages":[ { "role":"user", "content":"Hello!" } ] }'Streaming is enabled with:
{
"stream": true
}pip install -r requirements.txt
uvicorn api.index:app --reload --port 8000Open:
http://localhost:8000
| Variable | Default | Description |
|---|---|---|
MODEL_PATH | ./model | Path to the GGUF model |
CACHE_MB | 256 | KV cache size |
- CPU-only inference
- Cold-start model loading
- One active generation per instance
- Vercel Hobby plans have a 10-second execution limit
- Flatseek — Keyword Search
- Flatvec — Vector Search
- Flatask — RAG Runtime
- Flatlens — Data Visualization
- Flatbuild — LLM Training
- Flattune — LLM Fine-Tuning
- Flatrun — LLM Inference
Flatbot demonstrates that a complete conversational AI stack—from training a language model to serving it through a modern chat interface—can fit into a compact, fully open-source project.