---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-2
model-index:
- name: DragonLLM/Dragon-3B-Base-alpha
  results:

  - task:
      type: multiple-choice-qa
      name: ARC Challenge
    dataset:
      type: ai2_arc
      name: AI2 ARC (Challenge)
      config: ARC-Challenge
      split: test
    metrics:
    - type: accuracy
      name: Test accuracy
      value: 50.00

  - task:
      type: multiple-choice-qa
      name: ARC Easy
    dataset:
      type: ai2_arc
      name: AI2 ARC (Easy)
      config: ARC-Easy
      split: test
    metrics:
    - type: accuracy
      name: Test accuracy
      value: 76.01

  - task:
      type: commonsense-reasoning
      name: HellaSwag
    dataset:
      type: hellaswag
      name: HellaSwag
      split: validation
    metrics:
    - type: accuracy
      name: Acc
      value: 71.73

  - task:
      type: language-modeling
      name: LAMBADA (word prediction)
    dataset:
      type: lambada
      name: LAMBADA
      split: test
    metrics:
    - type: accuracy
      name: Acc
      value: 65.03

  - task:
      type: commonsense-reasoning
      name: PIQA
    dataset:
      type: piqa
      name: PIQA
      split: validation
    metrics:
    - type: accuracy
      name: Acc
      value: 79.11

  - task:
      type: information-extraction
      name: SWDE
    dataset:
      type: swde
      name: SWDE
      split: test
    metrics:
    - type: accuracy
      name: Acc
      value: 89.92

  - task:
      type: classification
      name: FDA
    dataset:
      type: fda
      name: FDA
      split: test
    metrics:
    - type: accuracy
      name: Acc
      value: 81.13

---
## Highlights

Dragon LLM introduces its new LLM Architecture. Built on a new hybrid GDN -Transformer that outperforms traditional architectures, it can power frugal, sovereign models that can be rapidly specialized on business data and use cases.

Dragon Architecture features :
- Very strong ability to remember past words in the sequence compared to other Hybrid approach, inspired by Hymba (NVIDIA)
- Ability to be used simultaneously by more users on equivalent hardware and better throughput on long-context scenario
- Extremely efficient learning
It has been been validated at large scale by the training of a 3B model on 3.5T tokens. It achieves comparable performance against smolLM-3B-Base and Qwen3-4B-Base on ARC, HellaSwag, LAMBADA, and PIQA, while trained on 3-5 time less data.

Why is this important?
- **Proves performance** → same performance with 3–5× less data.
- **Cut cost** : more users can be served on the same hardware
- Ability to deploy in secure environment with constraint on the hardware (even on CPU)
- **Scales better** : higher throughput and strong long-context handling (Long documents, files, codes or contracts).


How has Dragon LLM achieved this?
• By combining the best recent research papers on LLM architectures, cumulating gains across all processes, from deep layer optimization to attention head or kv cache management.
• Agile Team able to adapt quickly and test new ideas extremely fast
• Compute support by the EU Commission (euroHPC - JUPITER and Leonardo HPC)


What's next?
The next step is to deliver foundation models for this architecture :
• a 3B and 7B version of DragonBase trained on 10T+ tokens
• Chat version of these models
• Specialized versions for specific industry vertical such as Finance

If you want to know more and get updates on the project, follow us !

If you would like a comprehensive deep dive on the architecture : [read our blog post](https://open.substack.com/pub/dragonllm/p/inside-dragons-architecture?r=3j0al4&utm_campaign=post&utm_medium=web)

## Model Overview

![arch_long_blanc](https://cdn-uploads.huggingface.co/production/uploads/64f0a2ac5b9c8cdb17786783/IRSj0xkdkblSShP5oiLwP.png)


## Model Benchmark

|Benchmarks |Dragon	|Qwen3-4B	|SmolLM3|
|----|----|----|----|
|ARC Challenge	|50%	|51.28%	|**52.56%**|
|ARC Easy	|76.01%	|75.97%	|**76.81%**|
|HellaSwag	|71.73%	|54.46%	|**75.2%**|
|LAMBADA	|65.03%	|62.62%	|**65.05%**|
|PIQA	|**79.11%**	|77.86%	|78.84%|
|SWDE	|89.92%	|**91.99%**	|88.03%|
|FDA	|81.13%	|**86.75%**	|76.13%|
|Average	|**73.27%**	|71.56%	|73.23%|

All evaluations are performed using with lm-eval and few shot set to 0.

## Limitations

This model is a foundation model, trained on large-scale general-purpose text corpora. It has not been fine-tuned for any specific downstream task. As such:

It may produce inaccurate or misleading information, particularly for factual or time-sensitive queries.

It has no understanding of truth or intent and may generate biased, toxic, or harmful content inherited from its training data.

It is not suitable for direct use in safety-critical or decision-making contexts (e.g., healthcare, finance, law) without additional alignment or validation.

The model does not perform well on tasks requiring domain-specific expertise, numerical precision, or structured reasoning unless further fine-tuned.

Long or complex prompts may lead to loss of coherence or hallucinations as context length grows.

Fine-tuning, prompt-engineering, or evaluation on downstream tasks is recommended before any production use.

## Quickstart

Try it with:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "DragonLLM/Dragon-3B-Base-alpha"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

prompt = "Once upon a time, a valiant knight named Segurant set out on a quest to chase a dragon. He was"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

generated_ids = model.generate(
  **inputs,
  max_new_tokens=512,
)

print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```

## Setup

For better performance on GPU, we recommend using :
- [flash-linear-attention](https://github.com/fla-org/flash-linear-attention): the Gated DeltaNet Triton kernels  
Install with ```pip install flash-linear-attention```

If you use NVIDIA GPU, you can improve performance with :
- [flash-attention](https://github.com/Dao-AILab/flash-attention):  
Install with ```pip install flash-attn --no-build-isolation```

- [causal-conv1d](https://github.com/Dao-AILab/causal-conv1d): a short convolution is used as part of the Gated DeltaNet layer  
Install with ```pip install causal-conv1d```

- (optional, recommended only for A100) [flex-head-ha](https://github.com/xiayuqing0622/flex_head_fa): computing attention with different head dimensions for qk and vo, used for differential attention  
Install with ```pip install flex-head-fa --no-build-isolation```