---
language:
- en
- fr
- de
- it
- es
pipeline_tag: text-generation
tags:
- cygnisai
- llama-3.1
- model-merge
- unsloth
- enterprise-ready
- finetuned
- safety-aligned
license: apache-2.0
datasets:
- cygnisai/Cygnis-Alpha-2-Instruct-Mix
base_model:
- unsloth/Meta-Llama-3.1-8B-bnb-4bit
library_name: transformers
---

# Cygnis-Alpha-2-8B-v0.2 (Merge Version)

<div align="center" style="background:#06090f; border-radius:14px; border:1px solid #0f1e30; overflow:hidden; margin-bottom:20px;">
  <img src="https://huggingface.co/cygnisai/Cygnis-Alpha-2-8B-v0.1/resolve/main/Cygnis-alpha-2.png" width="100%" style="display:block;">
</div>

**Cygnis-Alpha-2-8B-v0.2** is a next-generation large language model developed by **CygnisAI**. This version is the result of a strategic **Model Merge**, engineered to surpass the native reasoning capabilities of Llama 3.1 8B while maintaining optimal computational efficiency for enterprise-scale deployments.

## Architecture & Development

The model is built upon the **Llama 3.1** architecture (Auto-regressive Transformer). The CygnisAI team leveraged **Unsloth** for accelerated **Supervised Fine-Tuning (SFT)** and applied advanced **Model Merging** techniques to sharpen technical and professional response accuracy.

| Feature | Specification |
| :--- | :--- |
| **Developer** | [CygnisAI](https://huggingface.co/cygnisai) |
| **Base Model** | Meta Llama 3.1 8B (Finetuned via Unsloth) |
| **Context Length** | 128k tokens |
| **Optimization** | Multilingual (Primary focus: EN/FR) |
| **Knowledge Cutoff** | December 2023 |
| **License** | Apache 2.0 |

## Performance & Benchmarks

Cygnis v0.2 has been rigorously tested against internal benchmarks to ensure production-grade stability. It shows significant improvements in **Instruction Following** for complex, multi-step tasks compared to the v0.1 release.

* **MMLU (Reasoning)**: ~69.8%
* **IFEval (Strict Instruction)**: ~81.2%
* **HumanEval (Coding)**: ~72.9%

## Quick Start

### Via Transformers (Pipeline)
```python
import transformers
import torch

model_id = "cygnisai/Cygnis-Alpha-2-8B-v0.2"

pipeline = transformers.pipeline(
    "text-generation", 
    model=model_id, 
    model_kwargs={"torch_dtype": torch.bfloat16}, 
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are Cygnis, the official enterprise AI assistant."},
    {"role": "user", "content": "Analyze the scalability requirements for our LLM infrastructure."}
]

print(pipeline(messages, max_new_tokens=256))
```

### Official Prompt Format
The model follows the standard Llama 3.1 chat template:
```text
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{user_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Responsibility & Safety

In alignment with **CygnisAI’s** commitment to responsible AI:
1.  **Guardrails**: The model integrates filters to mitigate malicious content, disinformation, and unintended biases.
2.  **Transparency**: While highly capable, Cygnis should not be used without human oversight for critical decision-making (Medical, Legal, Financial).
3.  **License**: This model is released under the **Apache License 2.0**, offering maximum flexibility for commercial and private use.

## Legal Notice
This model was finetuned using the **Unsloth** library. While the underlying architecture is based on Llama 3.1, the specific weights of Cygnis-Alpha-2-8B-v0.2 are provided under the Apache 2.0 terms.  
*Built with Llama.*

---

## Citation

```bibtex
@misc{cygnis_alpha_2_v0.1,
  author    = {Simonc-44},
  title     = {The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Simonc-44/Cygnis-Alpha-2-8B-v0.1}
  eprint={2204.05149},
  archivePrefix={arXiv},
}
```

---
**Professional Contact & Support**: [cygnisai/contact](https://huggingface.co/cygnisai)  
*Driven by innovation and corporate-grade rigor at CygnisAI.*