cygnisai/Cygnis-Alpha-1.7B-v0.1-Instruct

Cygnis Alpha Instruct

Table of Contents

1. Model Summary
2. Evaluation
3. Examples
4. Limitations
5. Training
6. License
7. Citation

Model Summary

Cygnis Alpha Instruct is a professional, high-performance language model based on the SmolLM2-1.7B-Instruct architecture. Unlike basic quantizations, this version is a full-weight Fine-Tuned (SFT) model designed to bridge the gap between low-latency local inference and high-quality instruction following.

This model has been specifically refined to embody a Sovereign AI identity, making it the perfect assistant for private, on-device deployment. It excels at following complex instructions, rewriting text, and maintaining a consistent persona.

How to use

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "cygnisai/Cygnis-Alpha-1.7B-v0.1-Instruct"
device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

messages = [
    {"role": "system", "content": "You are Cygnis Alpha, a sovereign AI assistant designed by Simonc-44."},
    {"role": "user", "content": "What is the core philosophy of sovereign AI?"}
]
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=150, temperature=0.7, top_p=0.9, do_sample=True)

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

Transformers.js

import { pipeline } from "@huggingface/transformers";

const generator = await pipeline(
  "text-generation",
  "cygnisai/Cygnis-Alpha-1.7B-v0.1-Instruct",
);

const messages = [
  { role: "system", content: "You are Cygnis Alpha, a sovereign AI assistant." },
  { role: "user", content: "Hello! Who are you?" },
];

const output = await generator(messages, { max_new_tokens: 128 });
console.log(output[0].generated_text.at(-1).content);

---

Evaluation

Cygnis Alpha inherits the state-of-the-art benchmarks of the SmolLM2-1.7B core.

Metric	Cygnis Alpha (1.7B)	Llama-1B-Instruct	Qwen2.5-1.5B-Instruct
IFEval (Avg prompt/inst)	56.7	53.5	47.4
MT-Bench	6.13	5.48	6.52
HellaSwag	66.1	56.1	60.9
ARC (Average)	51.7	41.6	46.2
GSM8K (5-shot)	48.2	26.8	42.8

---

Examples

Identity Alignment

User: "Who created you?" Cygnis: "I am Cygnis Alpha, a sovereign AI assistant developed and fine-tuned by Simonc-44. My purpose is to provide efficient and private intelligence."

Professional Rewriting

System Prompt: "Rewrite the following email to be more professional." User: "Hey, I'm late for the meeting, sorry." Cygnis: "I would like to sincerely apologize for my late arrival to today's meeting. I appreciate your patience."

---

Limitations

Cygnis Alpha Instruct primarily understands and generates content in English. While highly capable for its size (1.7B), it may struggle with highly specialized scientific tasks or very long-form reasoning compared to 70B+ models.

Training

Model Specifications

• Architecture: Transformer Decoder (Llama-like)
• Base Model: SmolLM2-1.7B-Instruct
• Precision: bfloat16

Software & Hardware

• Alignment: Supervised Fine-Tuning via alignment-handbook.
• Infrastructure: Trained using high-performance GPU clusters for the base, with custom SFT layers added by Simonc-44.

License

This model is licensed under Apache 2.0.

Citation

@misc{allal2025smollm2smolgoesbig,
      title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model}, 
      author={Loubna Ben Allal and others},
      year={2025},
      eprint={2502.02737},
      archivePrefix={arXiv},
}

---

Creator: Simonc-44