README.md

---
license: apache-2.0
language: [en]
library_name: safetensors
pipeline_tag: text-generation
tags: [hobbylm, mixture-of-experts, moe, sparse-moe]
---

# HobbyLM-Base (500M sparse-MoE foundation LM)

HobbyLM-Base is the foundation the whole family is built on: a 500M-parameter sparse Mixture-of-Experts decoder trained **from scratch** on FineWeb — no distillation, no borrowed weights. It exists to answer a simple question: how far can you get at the ~500M scale if you sweat the architecture and the training recipe instead of throwing tokens at the problem?

It's part of the **HobbyLM** family — a 500M sparse-MoE model (and its variants) built from scratch on a
hobby budget: FineWeb, a handful of Modal H100 hours, a lot of ablations, and a from-scratch Rust engine
([`hobby-rs`](https://github.com/harishsg993010/HobbyLM)) to run it on a laptop CPU.

## Intended use

A pretrained base model for text completion, and the checkpoint you fine-tune for downstream tasks. It is **not** instruction-tuned — for chat, use [HobbyLM-Chat](https://huggingface.co/rootxhacker/HobbyLM-Chat).

## Architecture

Every HobbyLM variant shares one core: a **sparse Mixture-of-Experts (MoE)** decoder in the modern
small-MoE style (DeepSeek-V3 / OLMoE lineage), where each design choice was picked by ablation rather
than by guesswork.

| Component | Value |
|---|---|
| Total parameters | ~500M (only a fraction is active per token) |
| Hidden size / layers | 768 / 16 (first FFN dense, the rest MoE) |
| Routed experts / active | 36 / top-6 (+ 1 always-on shared expert) |
| Attention | GQA, 12 query / 3 KV heads, decoupled head-dim 128, per-head QK-norm |
| Router | sigmoid gating, DeepSeek-V3 aux-loss-free load balancing, no top-k renorm |
| Positional | RoPE (θ up to 1e6 for the 8k-context checkpoints) |
| Tokenizer | GPT-2 byte-level BPE (50,304 vocab, sentinel-padded) |
| Optimizer | Muon on the 2-D + per-expert matrices, AdamW on everything else |

The full ablation log (QK-norm is the single biggest lever; aux-loss-free beats classic aux-loss;
≥32 experts and top-6 help; embedding-scaling hurt) lives in the project's architecture notes.

## Benchmarks

0-shot, 7-task average through our harness (see note below). HobbyLM was trained on **40B tokens** — a tiny
budget next to the comparison models — so the right way to read this table is *per training token*.

| Model | Params | Pretrain tokens | Avg (7-task) |
|---|---|---|---|
| SmolLM2-360M | 360M | ~4T | 56.29 |
| Qwen3-0.6B | 600M | ~36T | 54.78 |
| gemma-3-270m | 270M | — | 48.09 |
| pythia-410m | 410M | 300B | 45.34 |
| **HobbyLM-Base (500M)** | **500M** | **40B** | **44.05** |
| opt-350m | 350M | 180B | 43.61 |
| HobbyLM-130M (sibling) | 130M | 10B | 42.97 |
| MicroLlama-300M | 300M | 50B | 42.23 |
| gpt2 | 124M | — | 40.62 |
| pythia-160m | 160M | 300B | 38.60 |

Per-task (0-shot): HellaSwag 41.5 · LAMBADA 40.0 · SciQ 70.3 · PIQA 69.6 · ARC-easy 42.7
(ARC-challenge / WinoGrande sit near chance, as expected at this scale). Validation loss: **3.03** at 1k
context, **2.94** after the 8k context-extension.

The ranking tracks **pretraining tokens**, not parameters: the top models see 50–900× more data than we do.
In the classic ≤300B-token regime, HobbyLM leads per token — the 130M (10B tokens) beats MicroLlama-300M
(50B), opt-350m (180B) and pythia-160m (300B). Token budget, not architecture, is the gap.

> **How these were measured.** All language-model scores are **0-shot** through our own port of
> EleutherAI's `lm-evaluation-harness` (a custom `MoELMWrapper` that runs log-likelihood scoring over the
> HobbyLM MoE + GPT-2 tokenizer). Reference models in the comparison table were run through the **identical
> harness and task set**, so the numbers are apples-to-apples with ours — they are *not* copied from other
> model cards. We validated the harness against published cards (e.g. TinyLlama 52.75 vs card 52.99). These
> are small research models: read the numbers in context, not as leaderboard claims.

## Usage

### Python (PyTorch reference implementation)

HobbyLM is a custom sparse-MoE architecture — there's no `transformers` `AutoModel` for it, so load it with
the small reference implementation from the [GitHub repo](https://github.com/harishsg993010/HobbyLM):

```python
# HobbyLM is a CUSTOM sparse-MoE architecture, so load it with the reference implementation —
# NOT transformers.AutoModelForCausalLM (there is no AutoModel mapping for this arch).
# pip install torch safetensors tiktoken huggingface_hub
# git clone https://github.com/harishsg993010/HobbyLM && cd HobbyLM

import json, torch, tiktoken
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from hobbylm.config import ModelConfig
from hobbylm.model import MoETransformer
from hobbylm.generate import generate

repo = "rootxhacker/HobbyLM-Base"
cfg = ModelConfig(**{k: v for k, v in json.load(open(hf_hub_download(repo, "config.json"))).items() if k != "preset"})
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cfg.expert_backend = "grouped" if device.type == "cuda" else "bmm"

model = MoETransformer(cfg).to(device).eval()
model.load_state_dict(load_file(hf_hub_download(repo, "model.safetensors")))

enc = tiktoken.get_encoding("gpt2")
prompt = "The capital of France is"
ids = torch.tensor([enc.encode_ordinary(prompt)], device=device)
out = generate(model, ids, max_new_tokens=64, temperature=0.7, top_k=0, device=device,
               repetition_penalty=1.3)               # temperature=0.0 for greedy
print(enc.decode(out[0].tolist()))
```

### GGUF + hobby-rs (CPU)

GGUF builds (architecture `hobbylm`) live in [`rootxhacker/HobbyLM-gguf`](https://huggingface.co/rootxhacker/HobbyLM-gguf). They load
directly in the from-scratch `hobby-rs` CPU engine — **stock llama.cpp won't load them** without registering
the `hobbylm` architecture first.

```bash
hobby-rs --model HobbyLM-Base.gguf --prompt "..." --n 64
```

## Training

Pretrained on ~40B unique FineWeb tokens (8×H100), then context-extended 1k→8k (RoPE θ 1e4→1e6). Muon on the hidden + per-expert matrices, AdamW on the router/embeddings/norms; fp32 router; chunked-checkpointed cross-entropy to fit a larger batch.

## Limitations

- It's a ~500M base model on a 40B-token budget: fluent and factually-okay on easy questions, but it hallucinates and can repeat without a repetition penalty at decode time.
- Trained on English FineWeb; other languages and code are out of distribution.
- Not aligned or safety-tuned.

## License

Apache-2.0. Weights aren't a substitute for judgement — this is a research / hobby model at the 500M scale,
not a production system.