---
license: apache-2.0
language:
- en
tags:
- text-generation
- transformers
- safetensors
- causal-lm
- language-model
- canon-layers
- rope-yarn
datasets:
- codelion/sutra-10B
- allenai/dolma3_longmino_mix-50B-1025
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: dhara-250m-ar-base
  results:
  - task:
      type: text-generation
    dataset:
      name: PIQA
      type: piqa
    metrics:
    - name: Accuracy
      type: acc_norm
      value: 57.4
  - task:
      type: text-generation
    dataset:
      name: WinoGrande
      type: winogrande
    metrics:
    - name: Accuracy
      type: acc
      value: 51.3
  - task:
      type: text-generation
    dataset:
      name: TruthfulQA MC2
      type: truthfulqa_mc2
    metrics:
    - name: Accuracy
      type: mc2
      value: 50.13
  - task:
      type: text-generation
    dataset:
      name: BoolQ
      type: boolq
    metrics:
    - name: Accuracy
      type: acc
      value: 37.83
  - task:
      type: text-generation
    dataset:
      name: OpenBookQA
      type: openbookqa
    metrics:
    - name: Accuracy
      type: acc_norm
      value: 32.4
  - task:
      type: text-generation
    dataset:
      name: ARC-Easy
      type: arc_easy
    metrics:
    - name: Accuracy
      type: acc_norm
      value: 30.18
  - task:
      type: text-generation
    dataset:
      name: HellaSwag
      type: hellaswag
    metrics:
    - name: Accuracy
      type: acc_norm
      value: 27.16
  - task:
      type: text-generation
    dataset:
      name: ARC-Challenge
      type: arc_challenge
    metrics:
    - name: Accuracy
      type: acc_norm
      value: 25.51
  - task:
      type: text-generation
    dataset:
      name: MMLU (5-shot)
      type: mmlu
    metrics:
    - name: Accuracy
      type: acc
      value: 22.95
  - task:
      type: text-generation
    dataset:
      name: SciQ
      type: sciq
    metrics:
    - name: Accuracy
      type: acc
      value: 21.3
  - task:
      type: text-generation
    dataset:
      name: Average (10 tasks)
      type: average
    metrics:
    - name: Accuracy
      type: acc
      value: 35.62
---

# Dhara-250M-AR-Base

A 250M parameter autoregressive language model with Canon layers and 32K context length, designed as the base model for the Dhara masked diffusion LLM family.

## Table of Contents

- [Model Description](#model-description)
- [Architecture](#architecture)
- [Training Data](#training-data)
- [Training Details](#training-details)
- [Benchmark Results](#benchmark-results)
- [Context Extension Results](#context-extension-results)
- [Usage](#usage)
- [YaRN Context Extension](#yarn-context-extension)
- [Key Insights](#key-insights)
- [Limitations](#limitations)
- [Related Work](#related-work)
- [Citation](#citation)

## Model Description

Dhara-250M-AR-Base is an autoregressive language model built on the LLaMA3 architecture enhanced with **Canon layers** — causal 1D depthwise convolutions placed at all 4 positions (ABCD) within each transformer layer. The model was pretrained on 10.2 billion tokens and extended to 32K context length via progressive RoPE scaling, with built-in **YaRN** support for inference-time extension to 64K-128K.

This model serves as the AR base for the Dhara diffusion LLM pipeline, where the pretrained weights are transferred to a masked diffusion model (MDM) for parallel text generation.

### Key Features

- **Canon Layers (ABCD)**: O(n) depthwise convolutions at 4 positions per layer for enhanced local context mixing with only 0.26% parameter overhead
- **32K Native Context**: Extended from 4K via progressive RoPE scaling (3 stages)
- **YaRN Support**: Built-in inference-time context extension to 64K-128K without retraining
- **GQA**: Grouped Query Attention (12 heads, 4 KV heads) for efficient inference
- **QK Normalization**: RMSNorm on Q/K after RoPE for training stability
- **Logit Softcapping**: Prevents logit explosion during training

## Architecture

| Specification | Value |
|---|---|
| Parameters | 249.2M |
| Layers | 32 |
| Hidden Size | 768 |
| FF Dimension | 2,176 |
| Attention Heads | 12 |
| KV Heads | 4 (GQA) |
| Head Dimension | 64 |
| Context Length | 32,768 tokens (extendable to 128K via YaRN) |
| RoPE Theta | 8,000,000 |
| Position Encoding | RoPE with YaRN scaling support |
| Normalization | RMSNorm |
| Activation | SiLU (SwiGLU MLP) |
| Vocabulary | 49,152 tokens (custom BPE) |
| Tied Embeddings | Yes |
| Canon Positions | A, B, C, D (all 4) |
| Canon Kernel Size | 4 |
| Canon Parameters | 638,976 (0.26% of total) |

### Canon Layer Positions

Based on ["Physics of Language Models: Part 4.1"](https://arxiv.org/abs/2407.20311) by Zeyuan Allen-Zhu:

- **Position A**: After input LayerNorm, before attention
- **Position B**: Inside attention, applied separately to Q/K/V after linear projections
- **Position C**: After post-attention LayerNorm, before MLP
- **Position D**: Inside MLP, after gate*up product

Each Canon layer is a causal depthwise 1D convolution with residual connection, adding local sequential context mixing at O(n) cost.

### Layer Flow

```
x → LayerNorm → [Canon-A] → Attention([Canon-B on Q,K,V]) → + residual
x → LayerNorm → [Canon-C] → MLP([Canon-D]) → + residual
```

## Training Data

### Stage 1: Pretraining (10.2B tokens)

- **Dataset**: [codelion/sutra-10B](https://huggingface.co/datasets/codelion/sutra-10B)
- **Tokens**: 10.2 billion
- **Context Length**: 4,096 tokens
- **Content**: Diverse English text across science, technology, mathematics, social studies, arts, and language arts
- **Domain Distribution**: interdisciplinary (35%), technology (21%), science (14%), social studies (8%), mathematics (8%), life skills (5%), arts (4%), language arts (2%)

### Stage 2: Long Context Extension (900M tokens)

Progressive RoPE scaling across 3 phases using long-document data:

| Phase | Context | RoPE Theta | Tokens | Dataset |
|---|---|---|---|---|
| 1: 4K → 8K | 8,192 | 500,000 | 200M | [allenai/dolma3_longmino_mix-50B-1025](https://huggingface.co/datasets/allenai/dolma3_longmino_mix-50B-1025) (lc_synth) |
| 2: 8K → 16K | 16,384 | 2,000,000 | 200M | allenai/dolma3_longmino_mix-50B-1025 (lc_synth) |
| 3: 16K → 32K | 32,768 | 8,000,000 | 500M | allenai/dolma3_longmino_mix-50B-1025 (lc_synth) |

## Training Details

| Parameter | Pretraining | Context Extension |
|---|---|---|
| Tokens | 10.2B | 900M (3 phases) |
| Batch Size | 64 effective | 16 effective |
| Learning Rate | 3e-4 → 3e-5 (cosine) | 3e-5 → 1e-5 (cosine) |
| Warmup | 389 steps (1%) | 20 steps per phase |
| Optimizer | AdamW (betas=0.9, 0.95) | AdamW (betas=0.9, 0.95) |
| Weight Decay | 0.1 | 0.1 |
| Precision | BF16 | BF16 |
| Hardware | Single NVIDIA RTX PRO 6000 Blackwell (96GB) | Same |
| Pretraining Time | ~48 hours | ~6.5 hours total |
| Throughput | ~60,000 tokens/sec | 33,000-55,000 tokens/sec |

## Benchmark Results

Evaluated using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.11 (0-shot unless noted):

| Benchmark | Before Context Ext. | After Context Ext. | Delta |
|---|---|---|---|
| PIQA | 57.78% | 57.40% | -0.38 |
| WinoGrande | 48.78% | 51.30% | +2.52 |
| TruthfulQA (mc2) | 49.79% | 50.13% | +0.34 |
| BoolQ | 37.83% | 37.83% | 0.00 |
| OpenBookQA | 31.60% | 32.40% | +0.80 |
| ARC-Easy | 30.30% | 30.18% | -0.12 |
| HellaSwag | 27.95% | 27.16% | -0.79 |
| ARC-Challenge | 26.62% | 25.51% | -1.11 |
| MMLU (5-shot) | 22.95% | 22.95% | 0.00 |
| SciQ | 22.00% | 21.30% | -0.70 |
| **Average** | **35.56%** | **35.62%** | **+0.06** |

Context extension to 32K preserved short-context benchmark performance with negligible change.

## Context Extension Results

Perplexity measured on held-out PG19 (Project Gutenberg) long documents:

| Context Length | Before (4K trained) | After (32K extended) | Improvement |
|---|---|---|---|
| 4,096 | 33.03 | 35.65 | ~same |
| 16,384 | 163.64 | **38.60** | **4.2x better** |
| 32,768 | 702.64 | **41.57** | **16.9x better** |

Before context extension, perplexity exploded beyond 4K (33 → 703). After extension, perplexity stays nearly flat across all lengths (36 → 42), confirming the model can effectively use the full 32K context.

## Usage

### Basic Usage

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

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("codelion/dhara-250m-ar-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "codelion/dhara-250m-ar-base",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
)

device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)

# Generate text
prompt = "The most important discovery in physics was"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(
    inputs.input_ids,
    max_new_tokens=100,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.2,
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Long Context Usage (32K)

```python
# The model natively supports 32K context
long_text = "..." # up to 32K tokens
inputs = tokenizer(long_text, return_tensors="pt", truncation=True, max_length=32768).to(device)
outputs = model(inputs.input_ids)
```

## YaRN Context Extension

The model includes built-in YaRN (Yet another RoPE extensioN) support for inference-time context extension beyond 32K without retraining:

```python
from transformers import AutoModelForCausalLM

# Extend to 64K (2x)
model = AutoModelForCausalLM.from_pretrained(
    "codelion/dhara-250m-ar-base",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    rope_scaling={"type": "yarn", "factor": 2.0, "original_max_position_embeddings": 32768},
)

# Extend to 128K (4x)
model = AutoModelForCausalLM.from_pretrained(
    "codelion/dhara-250m-ar-base",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    rope_scaling={"type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768},
)
```

YaRN splits RoPE frequency dimensions into three groups — high frequencies (local positions) are preserved, low frequencies (global positions) are interpolated — giving better quality than naive scaling.

## Key Insights

1. **Canon Layers Add Local Context Cheaply**: All 4 Canon positions (ABCD) add depthwise causal convolutions throughout the model with only 0.26% parameter overhead, enhancing local sequential pattern recognition.

2. **Progressive RoPE Extension Works**: Three-stage context extension (4K → 8K → 16K → 32K) with increasing theta preserved short-context quality while enabling flat perplexity out to 32K.

3. **AR as Foundation for MDM**: This model is designed as a weight donor for masked diffusion model conversion. The embeddings, MLPs, and Canon layers transfer directly; only the attention patterns need retraining for bidirectional denoising.

4. **Efficient Training**: The full pipeline (10.2B pretraining + 900M context extension) completed in ~55 hours on a single GPU.

## Limitations

- This is a **base model** without instruction tuning — it generates text continuations, not answers
- Performance is limited by the 250M parameter scale and 10.2B token training budget
- The tokenizer is English-focused; non-Latin scripts fall back to byte-level encoding
- Long-context quality depends on having relevant content filling the context window

## Related Work

- [Scaling Pedagogical Pretraining to 10 Billion Tokens](https://huggingface.co/blog/codelion/scaling-pedagogical-pretraining-10-billion-tokens) — Prior work on scaling pretraining data
- [The Optimal Architecture for Small Language Models](https://huggingface.co/blog/codelion/optimal-model-architecture) — Architecture design principles used in this model
- [Dhara-70M](https://huggingface.co/codelion/dhara-70m) — Our 70M diffusion language model
- [Physics of Language Models: Part 4.1](https://arxiv.org/abs/2407.20311) — Canon layers (Zeyuan Allen-Zhu)

## Contact

For questions or feedback, please open a discussion on the [Hugging Face discussions page](https://huggingface.co/codelion/dhara-250m-ar-base/discussions).