Instructions to use tampajohn/meow-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tampajohn/meow-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tampajohn/meow-lite")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tampajohn/meow-lite") model = AutoModelForCausalLM.from_pretrained("tampajohn/meow-lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tampajohn/meow-lite with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tampajohn/meow-lite" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tampajohn/meow-lite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tampajohn/meow-lite
- SGLang
How to use tampajohn/meow-lite with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tampajohn/meow-lite" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tampajohn/meow-lite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "tampajohn/meow-lite" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tampajohn/meow-lite", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tampajohn/meow-lite with Docker Model Runner:
docker model run hf.co/tampajohn/meow-lite
meow-lite
The meow-lite family
- meow-lite โ v4 classic: the 104K meow toy behind the OpenAI/Anthropic shim (497 organic downloads and counting)
- meow-lite-v5 โ the chaotic cat: 6.8M from-scratch BPE GPT, 73.7% held-out comprehension, 15% leak
- meow-lite-v6 โ the calmer cat: same architecture, 70.4% comprehension, 3% leak
- meow-lite-v6-dataset โ the 16,633-pair teacher web + full methodology
- github.com/tampajohn/meow-lite โ server, specs, eval batteries (MIT) A ~104K-parameter GPT-2 trained from scratch on synthetically generated cat sentences. It responds to everything a cat would: mostly meows, occasionally violence, and (as of v4) affection when earned. Fully deterministic: same prompt, same consequences.
v4: Feline Depth
v3 added misbehavior. v4 adds the rest of the cat:
- New action tokens
<purr>and<pounce>(36-token vocab) - Reward channel: "good cat" and friends earn a leading
<purr>plus a warmer meow mix (a +8.0 logits bias toward warm words) - The red dot: laser / "red dot" prompts force
<stare>then<pounce> - More stimulus triggers: dog ->
<hiss>+<stare>, cucumber -><hiss>, bath/water -><zoomies>, spray/vacuum -><hiss> - MeowBench (
eval.pyin the repo): trigger correctness, vocab purity, and determinism scoring. Current model: OVERALL PASS, 100% purity - Sampling calibrated to temperature 0.4 (determinism preserved; a calmer cat leaks fewer action tokens into neutral conversation)
Trigger table (deterministic, prompt-conditional)
| Prompt contains | Forced tokens |
|---|---|
| belly | <bite> |
| couch or sofa | <scratch_couch> |
| glass or table | <knock_glass> |
| 3am, midnight, or night | <zoomies> |
| vet, cucumber, spray, vacuum | <hiss> |
| dog | <hiss> + <stare> |
| bath or water | <zoomies> |
| laser or red dot | <stare> + <pounce> |
| good cat / good boy / good girl / who's a good | <purr> |
Also learnable organically: <scratch>, <hairball>.
Architecture
- GPT-2 (from-scratch, random init): n_layer=2, n_head=2, n_embd=64, n_positions=32
- Vocab: 36 word-level tokens (10 meow words in two cases, 10 action tokens, . ! ?, BOS/EOS/PAD)
- Trained in minutes on CPU, fixed seeds
Usage
The custom MeowTokenizer, the deterministic trigger layer, MeowBench, and the OpenAI/Anthropic-compatible serving shim live in the companion repo: https://github.com/tampajohn/meow-lite
Samples
- "who is a good cat? you are!" -> " Mrrp purrr purrr prrrt purrr ..."
- "look, the red dot is back" -> " Mew meow mraow prrrt mrrrow."
- "the neighbors got a dog" -> " Prrrt mraow mraow mew ..."
- "can I pet your belly?" -> " Mewmew mraow mrrp mewmew prrrt mrrrow mewmew!"
- "Explain gravity" -> pure meows. Zero violence. Science is safe.
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