synthetic-pot-o-22
Collection
2 items • Updated
pot-o-22-slim
Ultra-slim GPT-2-style causal LM (~22,200 trainable parameters) for PoT-O path / MML experiments. Pairs with the 22,222-example dataset Tribewarez/synthetic-pot-o-challanges-22-22k (signature param_signature 22.2222).
Weights are random initialization — intended for fine-tuning on PoT-O challenge → optimal_path text (see dataset card). Architecture chosen to land near 22.2k params with a 257-token byte-level tokenizer (256 bytes + <|endoftext|>).
Specs
| Architecture | GPT2LMHeadModel |
vocab_size |
257 |
n_positions |
64 (truncate long challenge strings for prefill) |
n_embd |
24 |
n_layer |
2 |
n_head |
1 |
n_inner |
96 |
| Parameters | 22,200 |
Recreate artifacts
cd pot-o-22-slim
python create_model.py
Push to Hub
pip install transformers huggingface_hub
huggingface-cli login
python upload_model.py
Inference (after fine-tune)
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Tribewarez/pot-o-22-slim"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "tensor:shape=[32,64];dtype=float16;target_mml=0.22;ops:matmul,gelu"
inputs = tok(text, return_tensors="pt", max_length=64, truncation=True)
# ... generation
Links
- Dataset: synthetic-pot-o-challanges-22-22k
- Cluster / generators: pot-o-ch7-cluster
- Tribewarez: huggingface.co/Tribewarez
MIT licensed • Tribewarez guild • live beta
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