from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rovdetection/code-1b-pretrain-v3")
model     = AutoModelForCausalLM.from_pretrained("rovdetection/code-1b-pretrain-v3")

inputs = tokenizer("def fibonacci(n):", return_tensors="pt")
out    = model.generate(**inputs, max_new_tokens=100, temperature=0.8, do_sample=True)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Training summary


Architecture	GPT-2 (1.13B params)
Total steps	30,000
Peak LR	3e-5 (cosine with warmup)
Effective batch size	32 (gradient accumulation)
Precision	fp16 + 8-bit Adam (bitsandbytes)
Eval perplexity (held-out Python)	3.65

Dataset mix (Phase 4 — final 7k steps)

Dataset	Weight
bigcode/starcoderdata (Python)	35%
codeparrot/codeparrot-clean	25%
open-phi/programming_books_llama	25%
greengerong/leetcode	15%

Training phases

Phase	Steps	LR range	Notes
1	0 – 10,000	0 → 3e-5	Warmup + early descent
2–3	10,000 – 23,000	3e-5 → 4e-6	Cosine decay, baseline mix
4	23,000 – 30,000	4e-6 → ~0	Quality shift: StarCoder ↑35%

Repo structure

The repo root contains inference weights only (model.safetensors, tokenizer, config.json). The last-checkpoint/ subfolder contains the full training state (optimizer, scheduler, scaler, RNG) for resuming training.