Initial upload of Dhara-70M diffusion language model

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- text-generation
+- diffusion
+- language-model
+- causal-lm
+datasets:
+- HuggingFaceFW/fineweb-edu
+- allenai/dolma
+- mlfoundations/dclm-baseline-1.0
+model-index:
+- name: dhara-70m
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      name: HellaSwag
+      type: hellaswag
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 25.58
+  - task:
+      type: text-generation
+    dataset:
+      name: PIQA
+      type: piqa
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 51.58
+  - task:
+      type: text-generation
+    dataset:
+      name: WinoGrande
+      type: winogrande
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 49.64
+  - task:
+      type: text-generation
+    dataset:
+      name: ARC-Challenge
+      type: arc_challenge
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 24.83
+  - task:
+      type: text-generation
+    dataset:
+      name: MMLU
+      type: mmlu
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 23.85
+  - task:
+      type: text-generation
+    dataset:
+      name: TruthfulQA
+      type: truthfulqa_mc2
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 47.50
+---
+
+# Dhara-70M
+
+A 70M parameter diffusion language model optimized for high-throughput text generation with superior factuality.
+
+## Table of Contents
+- [Model Description](#model-description)
+- [Training Data](#training-data)
+- [Training Details](#training-details)
+- [Benchmark Results](#benchmark-results)
+- [Usage](#usage)
+- [Key Insights](#key-insights)
+- [Limitations](#limitations)
+- [Citation](#citation)
+
+## Model Description
+
+Dhara-70M is a novel diffusion language model that achieves:
+- **3.8x higher throughput** than autoregressive models
+- **Best-in-class factuality** on TruthfulQA (47.50%)
+- **10x training efficiency** via WSD (Warmup-Stable-Decay) conversion
+
+### Architecture
+
+| Specification | Value |
+|--------------|-------|
+| **Parameters** | 71.34M |
+| **Layers** | 32 |
+| **Hidden Size** | 384 |
+| **FF Dimension** | 1024 |
+| **Attention Heads** | 8 |
+| **KV Heads** | 4 (GQA) |
+| **Context Length** | 2048 tokens |
+| **Position Encoding** | RoPE |
+| **Normalization** | RMSNorm |
+| **Special Layers** | Canon (depthwise causal convolutions) |
+| **Generation Type** | Diffusion (parallel token generation) |
+
+## Training Data
+
+Dhara was trained in two stages:
+
+**Stage 1: AR Pretraining (1B tokens)**
+- 40% FinePDFs (400M tokens)
+- 30% DCLM Baseline (300M tokens)
+- 30% FineWeb-Edu (300M tokens)
+
+**Stage 2: WSD Conversion (100M tokens)**
+- Progressive block size warmup (1→4→32→64→1024)
+- MDLM diffusion objective
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| **AR Training Tokens** | 1 billion |
+| **WSD Conversion Tokens** | 100 million |
+| **Batch Size** | 128 effective (8 × 16 gradient accumulation) |
+| **Learning Rate** | 5e-4 (AR) / 5e-5 (WSD) |
+| **Optimizer** | AdamW |
+| **Schedule** | Cosine decay with 2% warmup |
+| **Precision** | BF16 |
+| **Hardware** | Single NVIDIA A40 GPU |
+| **Total Training Time** | ~20 hours |
+
+## Benchmark Results
+
+| Benchmark | Dhara-70M | GPT-2-70M | vs GPT-2 |
+|-----------|-----------|-----------|----------|
+| HellaSwag (0-shot) | 25.58% | 26.46% | -0.88% |
+| PIQA (0-shot) | 51.58% | 58.05% | -6.47% |
+| WinoGrande (0-shot) | 49.64% | 52.64% | -3.00% |
+| ARC-Challenge (0-shot) | **24.83%** | 22.27% | **+2.56%** |
+| MMLU (5-shot) | 23.85% | 25.77% | -1.92% |
+| TruthfulQA (0-shot) | **47.50%** | 45.83% | **+1.67%** |
+| GSM8K (5-shot) | 0.00% | 1.21% | -1.21% |
+| **Average** | **31.85%** | **33.18%** | -1.33% |
+
+### Inference Performance
+
+| Metric | Dhara-70M | GPT-2-70M | Advantage |
+|--------|-----------|-----------|-----------|
+| Time to First Token | 35.5 ms | ~25 ms | 1.4x slower |
+| Throughput | 183.5 tok/s | ~48 tok/s | **3.8x faster** |
+| Peak Memory | 0.24 GB | 0.15 GB | 1.6x higher |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("codelion/dhara-70m")
+model = AutoModelForCausalLM.from_pretrained("codelion/dhara-70m")
+
+# Generate text
+inputs = tokenizer("The future of AI is", return_tensors="pt")
+outputs = model.generate(
+    **inputs,
+    max_length=50,
+    do_sample=True,
+    temperature=0.8,
+    top_p=0.9,
+    pad_token_id=tokenizer.eos_token_id
+)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Batch Generation (High Throughput)
+
+```python
+# For batch generation, use larger batch sizes
+prompts = [
+    "The future of AI is",
+    "In recent years, machine learning has",
+    "The most important discovery in physics was",
+    "Climate change affects our planet by"
+]
+
+inputs = tokenizer(prompts, return_tensors="pt", padding=True)
+outputs = model.generate(
+    **inputs,
+    max_length=100,
+    do_sample=True,
+    temperature=0.7,
+    num_diffusion_steps=10  # Fewer steps = faster generation
+)
+
+for i, output in enumerate(outputs):
+    print(f"Prompt {i+1}: {tokenizer.decode(output, skip_special_tokens=True)}")
+```
+
+## Key Insights
+
+1. **Throughput vs Accuracy Trade-off**: Dhara trades 1.33% average accuracy for 3.8x higher throughput, making it ideal for batch processing tasks.
+
+2. **Superior Factuality**: Dhara excels on TruthfulQA (+1.67% vs GPT-2), suggesting diffusion models may reduce hallucinations through bidirectional context.
+
+3. **Reasoning Advantage**: ARC-Challenge +2.56% indicates strong performance on reasoning tasks.
+
+4. **WSD Efficiency**: Converting an AR model to diffusion via WSD uses 10x fewer tokens than training from scratch with equivalent quality.
+
+5. **Canon Layers Help**: The depthwise causal convolutions (Canon layers) improve factuality and reasoning with only 0.13% parameter overhead.
+
+## When to Use Dhara
+
+**Choose Dhara when:**
+- Batch generation throughput matters
+- Factual accuracy is critical
+- You have an existing AR checkpoint to convert
+
+**Choose AR models when:**
+- Interactive latency is critical
+- Sequential reasoning is important (math, coding)
+- Memory is constrained
+
+## Limitations
+
+- Lower performance on sequential reasoning tasks (GSM8K: 0.00%)
+- Higher memory usage due to bidirectional attention
+- Slightly higher time-to-first-token latency
+- Best suited for batch rather than interactive use cases
+
+## Citation
+
+```bibtex
+@article{sharma2025dhara,
+  title={Dhara: Optimal Architecture for Efficient Diffusion Language Models},
+  author={Sharma, Asankhaya},
+  year={2025},
+  url={https://huggingface.co/codelion/dhara-70m}
+}
+```
+
+## Related Work
+
+- [Width vs Depth: The Optimal Architecture for Small Language Models](https://huggingface.co/blog/codelion/optimal-architecture) - Blog post describing this work
+- [The 1 Billion Token Challenge: Optimal Dataset Mixing](https://huggingface.co/blog/codelion/optimal-dataset-mixing) - Our previous work on optimal pretraining data
+- [GPT-2-70M](https://huggingface.co/codelion/gpt-2-70m) - Our previous model from optimal pretraining experiments
+
+## Contact
+
+For questions or feedback, please open an issue on the [Hugging Face model page](https://huggingface.co/codelion/dhara-70m).

config.json

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+{
+  "architectures": [
+    "DharaCanonForMaskedDiffusion"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "canon_activation": false,
+  "canon_bias": false,
+  "canon_kernel": 4,
+  "canon_residual": true,
+  "canon_set": "AC",
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1024,
+  "mask_epsilon": 0.001,
+  "mask_token_id": 50256,
+  "max_position_embeddings": 2048,
+  "model_type": "dhara_canon",
+  "num_attention_heads": 6,
+  "num_diffusion_steps": 1000,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 6,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.2",
+  "use_cache": false,
+  "use_flash_attention": false,
+  "use_xformers": false,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.55.2",
+  "use_cache": false
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:138820db3e8e59ed037924f14f9739ca9667e406465fc236fa9765691386f5fc
+size 304219496

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

@@ -0,0 +1,21 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}