Modelpublic

freakyskittle/oxidize-trader-7m

sourceHugging Faceapache-2.0updated 7d agoView on Hugging Face

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Model Card

oxidize-trader-7m

From-scratch GPT trained with oxidize-training (C + OpenBLAS). 7,432,704 parameters, F32.

This model was mainly for research and is NOT supposed to be used at all for anything. Can only be used in Oxidize-c (github.com/Zapdev-labs/Oxidize.git)

Weights are in `model.safetensors` as GPT-2 tensors (Conv1D layout, tied wte). Native trader.bin (OXTR) and vocab.bin are included for the C trainer.

It is a language model of ticket text, not a working stock picker.

Files

File	What
`model.safetensors`	GPT-2 F32 weights (29 MB)
`config.json`	`GPT2LMHeadModel`, 8L / 8H / 256d / 4096 vocab
`tokenizer.json` / `vocab.json` / `merges.txt`	byte-level BPE (ids match C `vocab.bin`)
`generation_config.json`	greedy, BOS 256, EOS 257
`trader.bin`	original OXTR checkpoint
`vocab.bin`	original C BPE merges

Load

python

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "freakyskittle/oxidize-trader-7m"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)
ids = tok("AAPL 2026-09-10 close 228 RSI 32 NEWS: fed holds. ACTION=", return_tensors="pt")
out = model.generate(**ids, max_new_tokens=16)
print(tok.decode(out[0], skip_special_tokens=True))

Or the C trainer:

bash

./oxidize-training/bin/oxidize-training sample \
  --ckpt trader.bin --vocab vocab.bin \
  --prompt "AAPL 2026-09-10 close 228 RSI 32 NEWS: fed holds. ACTION="

Size

8 layers, 8 heads, width 256, context 256, vocab 4096. GELU-tanh (gelu_new). Tied embeddings. Train: 2200 steps, loss 8.32 → 3.89.

What it actually is

Tickets look like SYM … NEWS: … ACTION=BUY|SELL|HOLD. Labels are next-day close vs ±0.6%. Last picks run printed BUY on all 20 names. Do not size real money.