qwen25-0.5b-fineweb-edu-10bt

qwen25-0.5b-fineweb-edu-10bt

A baseline model for TDT comparison experiments, pretrained from scratch with LitGPT on FineWeb-Edu sample-10BT.

Purpose

These models were created as baselines for comparison experiments with TDT. Their weights come from conventional gradient-based autoregressive language model training and provide a reference for comparing TDT training or adaptation methods and low-bit variants. The released checkpoints themselves were not trained with TDT.

The name qwen25-0.5b identifies the architecture. These models use the Qwen2.5-0.5B architecture and tokenizer, but their initial training did not start from official Qwen pretrained weights. The FineWeb-Edu stage starts from random initialization; the FineMath variants continue training from that FineWeb-Edu checkpoint.

Architecture

The configured maximum of 32,768 positions is an architectural setting. Training used sequences of 2,048 tokens; long-context performance has not been established.

Data preparation and training objective

The same prepare_fineweb_edu.py script is used for FineWeb-Edu and FineMath.

• Read the text column from Parquet files and skip empty text.
• Tokenize with the Qwen/Qwen2.5-0.5B tokenizer, without BOS and with EOS at the end of each document.
• Create streaming chunks with litdata.optimize and TokensLoader.
• Load blocks of 2,049 tokens: use the first 2,048 as inputs and the shifted 2,048 as targets, minimizing next-token cross-entropy.
• Shuffle training data, leave validation data unshuffled, and use seed 42.

Use in comparison experiments and limitations

These are base models for text completion. The recorded pipeline does not include conversational supervised fine-tuning, instruction tuning, or RLHF. A chat template in the tokenizer does not establish that the model was trained to converse.

The training configurations use split_names: [train, train], so validation reads from the same split as training. Reported validation losses are not independent held-out results. For TDT comparisons, control the initial weights, tokenizer, data splits, token budget, and evaluation conditions, and use a separate evaluation dataset. This card does not report TDT comparison results or mathematics benchmark scores.

Data sources are FineWeb-Edu and, for the FineMath variants, FineMath. Both source datasets list odc-by in their public metadata. Refer to their dataset cards for terms and attribution details.

Related models

Code: mssfj/lowbit-math-reasoning

Training data and configuration

Initial weights are randomly initialized. pretrain.sh downloads only the tokenizer from the official Qwen model, and initial_checkpoint_dir is unset. With resume: auto, an existing checkpoint in the output directory is used to resume training.

• Source data: HuggingFaceFW/fineweb-edu, sample-10BT (sample/10BT/*.parquet).
• Pretokenized data: mssfj/qwen25-0.5b-fineweb-edu-10bt. This is a dataset repository with the same ID as the model repository.
• Training token budget: 10,000,000,000. The source sample name 10BT should be distinguished from the actual corpus token count under the Qwen tokenizer.

The table below describes the current YAML configuration. It has not been fully reconciled with the execution logs for the published weights.

The script exports the end-of-training final checkpoint. This is not an automatically selected minimum-validation-loss checkpoint. Losses and benchmark results for this FineWeb-Edu model have not been verified and are not reported here.

Reproduction code

pretrain.sh contains data download, training, conversion, and upload steps. Some preprocessing steps are commented out. The training command is:

cd mylitgpt
litgpt pretrain \
  --config config_hub/pretrain/qwen25-0.5b-fineweb-edu-10bt.yaml \
  --out_dir out/pretrain/qwen25-0.5b-fineweb-edu-10bt

Checkpoint format and usage

This repository uses Hugging Face Transformers format with model.safetensors. The export pipeline removes optimizer state from the training final checkpoint, converts LitGPT weight names to HF names, and saves the result.

The example explicitly disables weight tying to handle a published configuration that may differ from the training setup.

import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer

repo = "mssfj/qwen25-0.5b-fineweb-edu-10bt"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = (
    torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
) if device == "cuda" else torch.float32
config = AutoConfig.from_pretrained(repo)
config.tie_word_embeddings = False
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, config=config, torch_dtype=dtype
).to(device).eval()
inputs = tokenizer(
    "The answer to 2 + 3 is",
    return_tensors="pt", add_special_tokens=False
).to(device)
with torch.inference_mode():
    output = model.generate(
        **inputs, max_new_tokens=64, do_sample=False,
        pad_token_id=tokenizer.eos_token_id
    )
print(tokenizer.decode(
    output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True
))