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

Name: Cygnis-Alpha-1.7B-v0.1-Instruct
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Cygnis Alpha Instruct

Model Summary
Evaluation
Examples
Limitations
Training
License
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

python

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

javascript

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

bibtex

@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

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