GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
plimb
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
plimb
Model Tree
Input Modalities
Output Modalities
Supported Functionality
A GGUF version (for llama.cpp, Ollama, LM Studio) is available thanks to mradermacher: mradermacher/gladios-tiny.story-0.1B-GGUF
gladios-tiny.story-0.1B is a decoder-only Transformer (GPT-2-style architecture, ~124M / 0.1B parameters) trained from scratch on the full TinyStories dataset (~2.1 million children's stories, ~470M tokens).
The model uses the GPT-2 tokenizer (50,257-token vocabulary) and generates short, coherent stories in English, in the style of the training data.
👉 Try it without installing anything: Gradio demo (ZeroGPU) 👉 Full training code: github.com/plimb-ai/gladios-tiny-story
| Type | Decoder-only Transformer (GPT-2 architecture) |
| Parameters | ~124M (0.1B) |
| Layers | 12 |
| Attention heads | 12 |
| Embedding dim | 768 |
| Context length | 512 tokens |
| Tokenizer | GPT-2 (tiktoken / 50,257-token vocab) |
| Weights | safetensors |
python
from transformers import pipeline gen = pipeline("text-generation", model="plimb/gladios-tiny.story-0.1B")print(gen( "Once upon a time", max_new_tokens=200, do_sample=True, temperature=0.8, top_k=50,)[0]["generated_text"])Or directly with AutoModelForCausalLM:
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model = AutoModelForCausalLM.from_pretrained("plimb/gladios-tiny.story-0.1B")tok = AutoTokenizer.from_pretrained("plimb/gladios-tiny.story-0.1B") ids = tok("Once upon a time", return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=200, do_sample=True, temperature=0.8, top_k=50, pad_token_id=tok.eos_token_id)print(tok.decode(out[0], skip_special_tokens=True))train split)<|endoftext|> tokenMIT
from transformers import pipeline gen = pipeline("text-generation", model="plimb/gladios-tiny.story-0.1B")print(gen( "Once upon a time", max_new_tokens=200, do_sample=True, temperature=0.8, top_k=50,)[0]["generated_text"])from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model = AutoModelForCausalLM.from_pretrained("plimb/gladios-tiny.story-0.1B")tok = AutoTokenizer.from_pretrained("plimb/gladios-tiny.story-0.1B") ids = tok("Once upon a time", return_tensors="pt").input_idsout = model.generate(ids, max_new_tokens=200, do_sample=True, temperature=0.8, top_k=50, pad_token_id=tok.eos_token_id)print(tok.decode(out[0], skip_special_tokens=True))