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AuthRan/
AshuGPT-124M-base

Name: AshuGPT-124M-base
Availability: InStock

HuggingFaceFW/fineweb-edu

mit

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AshuGPT-124M-base

The base checkpoint of AshuGPT — a decoder-only transformer written from scratch in PyTorch, with no transformers, no AutoModel, and no Trainer.


Parameters	123,587,328
Trained on	2.46B tokens of FineWeb-Edu
Steps	20,000
Validation loss / perplexity	3.1583 / 23.53
Hardware	one RTX 2080 Ti, ~27 hours, fp16
Architecture	RoPE, RMSNorm, SwiGLU, causal attention with a KV cache
Tokenizer	GPT-2 tiktoken vocabulary (50,304 padded)

This is a base model

It continues text. It does not answer questions and does not follow instructions, because nothing has taught it to yet — that is what the fine-tuning stages in the repo do. Give it the start of an expository paragraph, not a question.

If you want the model that answers and holds a conversation, it is live at AshGPT.

Its facts are unreliable at this size, and the repo measures that rather than hedging it: fluent form and correct content are separate axes. Unedited samples and an honest read on what it learned and what it did not are in results/.

Usage

pip install git+https://github.com/AuthRan/AuthLLM.git tiktoken

python

import torch
from huggingface_hub import hf_hub_download
from ashugpt.inference.generate import generate
from ashugpt.tokenizer.tiktoken_bpe import TiktokenBPETokenizer
from ashugpt.training.checkpoint import load_model_for_inference

path = hf_hub_download("AuthRan/AshuGPT-124M-base", "model.pt")
model = load_model_for_inference(path).eval()
tok = TiktokenBPETokenizer()

ids = torch.tensor([tok.encode("The process of photosynthesis", add_bos=True)])
out = generate(model, ids, max_new_tokens=120, temperature=0.8, top_k=50, eos_id=tok.eos_id)
print(tok.decode(out[0].tolist()))

model.pt is inference-only — the optimizer state is stripped by scripts/export_inference.py, which is why it is 494MB rather than 1.5GB. The model is rebuilt from the config saved inside the checkpoint, so nothing is hard-coded to a particular size.

How it was built

The full month-by-month record, including the failures, is in the repo: what went wrong is the most useful file. Five failed launches, an OOM, a reboot that killed everything, and a status reporter that published a confidently wrong ETA.