GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
CastIronMind
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
CastIronMind
Model Tree
Input Modalities
Output Modalities
Supported Functionality
The smallest My Little Pony language model we could find: 32 million parameters and 64 MB of weights. This is the pretrained base checkpoint: a plain next-token model, not a chat assistant.
| field | value |
|---|---|
| Type | Causal Language Model |
| Training stage | Pretraining from scratch |
| Architecture | Llama |
| Parameters | 31,990,272 (32M) |
| Layers | 8 |
| Hidden size | 512 |
| Attention heads | 16 (2 key/value heads) |
| Head dimension | 32 |
| Intermediate size | 1,536 |
| Context length | 1,024 tokens |
| Vocabulary | 16,384 (byte-level BPE) |
| Tied embeddings | yes |
| Precision | bfloat16 |
| License | Apache 2.0 |
| model | parameters | weights | best for |
|---|---|---|---|
| LissaNano-Base | 32M | 64 MB | continued pretraining, research |
| LissaNano-Instruct | 32M | 64 MB | phones, single-board computers, embedded use |
| LissaBrat-Base | 103M | 206 MB | continued pretraining, research |
| LissaBrat-Instruct | 103M | 206 MB | laptops, longer and more coherent text |
| Lissa 1.5B Instruct | 1.45B | 2.9 GB | a full roleplay partner |
| Lissa 1.5B Base | 1.45B | 2.9 GB | pretraining, research |
text
pip install torch transformersNo custom kernels and no exotic dependencies: this is a plain Llama checkpoint and loads with any recent transformers.
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CastIronMind/LissaNano-Base"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)model.eval() prompt = "Twilight Sparkle opened the library door and"ids = tok.encode(prompt, add_special_tokens=False) out = model.generate(torch.tensor([ids]), max_new_tokens=120, do_sample=True, temperature=0.8, top_p=0.9, repetition_penalty=1.1, pad_token_id=0, eos_token_id=1)print(tok.decode(out[0][len(ids):], skip_special_tokens=True))This is a base language model: feed it plain text and it continues it. There is no chat template and no role markers.
The special tokens 2 <system>, 3 <user> and 4 <assistant> exist in the vocabulary but are not part of the pretraining format, so do not use them here. If you need dialogue, use the Instruct checkpoint instead.
Pretraining. Fandom prose and forums from the My Little Pony community, about 1 billion unique tokens, two epochs.
This checkpoint is the result of that pretraining stage only.
A frozen sample of 313 texts from Fimfarchive, about 10 million tokens, held out from training and identical for every model in the family.
| metric | value |
|---|---|
| Perplexity | 31.88 |
| Bits per character | 1.2091 |
| item | value |
|---|---|
| Training hardware | one consumer GPU |
| Authors | one person |
| Weights on disk | 64 MB (bfloat16) |
| License | Apache 2.0 |
Apache License 2.0. Use it, fine-tune it, quantize it, embed it, and publish your own versions.
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CastIronMind/LissaNano-Base"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)model.eval() prompt = "Twilight Sparkle opened the library door and"ids = tok.encode(prompt, add_special_tokens=False) out = model.generate(torch.tensor([ids]), max_new_tokens=120, do_sample=True, temperature=0.8, top_p=0.9, repetition_penalty=1.1, pad_token_id=0, eos_token_id=1)print(tok.decode(out[0][len(ids):], skip_special_tokens=True))