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title: MonarchSLM
emoji: "\U0001F451"
colorFrom: yellow
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
license: mit
tags:
- julia
- lux
- slm
- philosophy
- openai-compatible
- bpe
- monarch-mixer
- rmsnorm
- swiglu
---
# MonarchSLM
A Monarch Mixer decoder-only model (sub-quadratic sequence mixing, 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, 8 layers, 8 Monarch heads
- **Sequence mixing**: Multi-head Monarch Matrix (sub-quadratic) + Causal Depthwise Conv + Learned Gate
- **Tokenizer**: BPE (2000 tokens)
- **Framework**: Lux.jl (explicit parameter/state management)
- **Normalization**: RMSNorm (pre-norm)
- **Feed-forward**: SwiGLU activation
- **Weight tying**: Shared embedding/output projection
- **Inference**: CPU-only, no Lux dependency at runtime (pure NNlib)
## Environment Variables
- `HF_REPO` — HuggingFace model repo (default: `LisaMegaWatts/MonarchSLM`)
- `PORT` — Server port (default: `7860`)
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