---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- text-generation
- causal-lm
- transformer
- argonne
- pretrained
- base-model
pipeline_tag: text-generation
---

# Argonne 3.0-base

Argonne 3.0-base is a 2.88B-parameter decoder-only transformer language model from the Argonne 3.x family. It is a *base* (foundation) checkpoint trained from scratch on FineWeb-derived web text and is intended as a starting point for further continued pretraining, supervised fine-tuning, or preference optimization.

The architecture combines grouped-query attention with several stability-oriented additions (QK-norm, V-norm, sandwich norms, interleaved local/global attention, and a final logit softcap). Weights are stored in bf16 and split across 5 safetensor shards so the model can be loaded with `transformers` on commodity hardware.

## Model architecture

| Component | Specification |
|-----------|---------------|
| **Parameters** | 2,882,162,688 (~2.88B) |
| **Layers** | 24 transformer blocks |
| **Hidden size** | 3,072 |
| **Attention heads** | 12 query / 4 key-value (GQA) |
| **Head dimension** | 256 |
| **Feed-forward** | SwiGLU MLP, 8,192 intermediate dim |
| **Attention pattern** | Interleaved local/global causal attention |
| **Local attention window** | 256 tokens (every other layer) |
| **Normalization** | RMSNorm with QK / V / sandwich norms |
| **Position encoding** | RoPE (θ = 1,000,000) |
| **Logit stabilization** | Final logit softcap = 15.0 |
| **Context length** | 1,024 tokens |
| **Vocabulary size** | 151,669 |
| **Tied embeddings** | Yes (input ↔ output) |

## Training details

| Item | Value |
|------|-------|
| **Stages** | Two-stage causal language modeling (pretrain → continued pretrain) |
| **Total optimizer steps** | 329,148 |
| **Tokens processed (cumulative)** | 76,050,702,336 (~76.05B) |
| **Stage 1 tokens (pretrain)** | 20,839,021,454 (~20.84B, single epoch) |
| **Stage 2 tokens (continued pretrain)** | 55,211,688,156 (~55.21B, single epoch) |
| **Sequence length** | 1,024 tokens |
| **Batch size per GPU** | 38 |
| **Gradient accumulation steps** | 2 |
| **Data-parallel world size** | 3 GPUs |
| **Effective batch** | 233,472 tokens / step |
| **Optimizer** | AdamW (β₁=0.9, β₂=0.95, weight decay 0.1) |
| **Peak learning rate** | 3.0e-4 |
| **Min LR ratio** | 0.1 |
| **Schedule** | Warmup-Stable-Decay (WSD); 1,000 warmup steps, 0 cooldown (stable phase only) |
| **Gradient clipping** | 1.0 |
| **Precision** | bf16 autocast (weights in fp32, optimizer states in fp32) |
| **`torch.compile`** | Enabled (default mode) |
| **Gradient checkpointing** | Enabled |
| **Flash attention** | Enabled (kernels fall back gracefully if unavailable) |
| **Final-slice average train loss** | 2.5168 |
| **Checkpoint dtype on Hub** | bfloat16 |
| **Weight format on Hub** | 5 sharded safetensors + index |
| **Hardware** | 3× NVIDIA H200 GPUs (DDP) |
| **Random seed** | 444 |

### Stage 1 — pretrain (`pretrain.py`)
- Cold-started randomly initialized weights.
- One full epoch over the FineWeb pretraining shard (20.84B tokens).
- 1,000-step linear warmup followed by the WSD stable phase at LR 3.0e-4.

### Stage 2 — continued pretrain (`continue_pretrain.py`)
- Resumed from the stage-1 checkpoint with a fresh optimizer / scheduler (data cursor reset to the new shard).
- One full epoch over the FineWeb CC-MAIN-2025-21 shard (55.21B tokens).
- Same hyperparameters as stage 1, no additional warmup.

## Training data

| Item | Value |
|------|-------|
| **Pretrain corpus** | FineWeb (tokenized with the Qwen3 tokenizer); see [HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) |
| **Continued-pretrain corpus** | FineWeb CC-MAIN-2025-21 dump (Qwen3 tokenizer); see [HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) |
| **Tokenizer source** | [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) (151,669-token vocab) |

## Tokenizer

This model reuses the Qwen3 tokenizer (vocabulary size 151,669) through the `Qwen2Tokenizer` compatibility class. The tokenizer files are bundled with the checkpoint so no extra download is required.

## Source code

Built from the GitHub main branch: https://github.com/PursuitOfDataScience/ArgonneAI/tree/main

Key scripts used to produce this checkpoint:
- [`model.py`](https://github.com/PursuitOfDataScience/ArgonneAI/blob/main/model.py) — the `ArgonneModel` / `ArgonneConfig` architecture (bundled here as `model.py`)
- [`pretrain.py`](https://github.com/PursuitOfDataScience/ArgonneAI/blob/main/pretrain.py) — stage 1 DDP pretraining loop
- [`continue_pretrain.py`](https://github.com/PursuitOfDataScience/ArgonneAI/blob/main/continue_pretrain.py) — stage 2 continued-pretraining loop

## Training loss curve

The figure below tracks loss, perplexity, and learning rate against cumulative training tokens across both stages.

![Training loss curve](plots/loss_plot.png)

The warmup-stable-decay schedule is visible in the LR panel: 1,000 linear warmup steps to 3.0e-4 followed by a flat stable phase (cooldown was set to 0 for this run).

## Inference

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "PursuitOfDataScience/argonne-3.0-base"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
)

prompt = "Write a short paragraph about scientific computing at Argonne National Laboratory."
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(model.device)

output_ids = model.generate(
    input_ids,
    max_length=input_ids.shape[1] + 128,
    temperature=0.8,
    top_p=0.95,
    top_k=50,
    do_sample=True,
)
print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

```

## Usage notes

- Load with `trust_remote_code=True` so the custom `ArgonneModel` / `ArgonneConfig` classes (`model.py`) are registered.
- The custom `generate` method on `ArgonneModel` uses `max_length` (total sequence length) rather than `max_new_tokens`; see the snippet above for the recommended pattern.
- This is a *base* model: no instruction tuning, alignment, or safety filtering has been applied. Outputs can include factually incorrect, biased, or unsafe text.
- Weights are published as 5 bf16 safetensor shards with a `model.safetensors.index.json` weight map for sharded loading.
- The published context length is 1,024 tokens. RoPE uses θ = 1,000,000 so the same checkpoint can be extended to longer contexts in follow-on stages.
- Switch to greedy decoding (`do_sample=False`) if you want deterministic output.

## Limitations

- Trained on web data only; no instruction following, dialogue, or tool use.
- 1,024-token context limits multi-document or long-form tasks without further long-context training.
- Loss plateaued around ≈2.5 (~12 PPL) on FineWeb — typical for a 2.88B model trained on ~76B tokens, but well above frontier-scale models.

## Citation

```bibtex
@misc{argonne30base,
  author = {PursuitOfDataScience},
  title = {Argonne 3.0-base},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/PursuitOfDataScience/argonne-3.0-base}
}
```