README.md

---
title: JuliaSLM
emoji: 🏛️
colorFrom: purple
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
license: mit
tags:
  - julia
  - lux
  - slm
  - philosophy
  - openai-compatible
  - bpe
  - rope
  - rmsnorm
  - swiglu
---

# JuliaSLM

A decoder-only transformer (RoPE, RMSNorm, SwiGLU) trained on classical philosophy texts, implemented in Julia with Lux.jl. Serves an OpenAI-compatible API with streaming support.

## Endpoints

- `GET /` — Health check and model info
- `GET /v1/models` — List available models
- `POST /v1/chat/completions` — Generate text (supports streaming, top-k, top-p)

## Usage

```bash
# Non-streaming
curl -X POST https://your-space.hf.space/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "the nature of"}], "max_tokens": 200}'

# Streaming
curl -X POST https://your-space.hf.space/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages": [{"role": "user", "content": "the nature of"}], "stream": true, "temperature": 0.7, "top_k": 40}'
```

## Architecture

- **Model**: ~5M params, 256d embed, 6 layers, 4 heads
- **Tokenizer**: BPE (2000 tokens)
- **Framework**: Lux.jl (explicit parameter/state management)
- **Positional encoding**: Rotary Position Embeddings (RoPE)
- **Normalization**: RMSNorm (pre-norm)
- **Feed-forward**: SwiGLU activation
- **Weight tying**: Shared embedding/output projection
- **Inference**: CPU-only, no Lux dependency at runtime (pure NNlib)

## Required HF Model Repo Files

Upload these to `LisaMegaWatts/JuliaSLM` (or set `HF_REPO` env var):

- `final.jld2` — Trained model checkpoint (parameters)
- `config.toml` — Model architecture configuration (from 5m.toml)
- `vocab.json` — BPE vocabulary (dict format: `{"token": id, ...}`)
- `merges.txt` — BPE merge rules

## Environment Variables

- `HF_REPO` — HuggingFace model repo (default: `LisaMegaWatts/JuliaSLM`)
- `PORT` — Server port (default: `7860`)