141 lines
4.8 KiB
Markdown
141 lines
4.8 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
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tags:
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- finetuned
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- sft
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- smollm2
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- sovereign-ai
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- safetensors
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- onnx
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- transformers.js
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---
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# Cygnis Alpha Instruct
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<div align="center" style="background:#06090f; border-radius:14px; border:1px solid #0f1e30; overflow:hidden; margin-bottom:20px;">
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<img src="https://huggingface.co/cygnisai/Cygnis-Alpha-1.7B-v0.1-Instruct/resolve/main/Cygnis-Alpha-Instruct.png" width="100%" style="display:block;">
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</div>
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## Table of Contents
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1. [Model Summary](#model-summary)
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2. [Evaluation](#evaluation)
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3. [Examples](#examples)
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4. [Limitations](#limitations)
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5. [Training](#training)
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6. [License](#license)
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7. [Citation](#citation)
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## Model Summary
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**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.
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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.
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### How to use
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#### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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checkpoint = "cygnisai/Cygnis-Alpha-1.7B-v0.1-Instruct"
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device = "cuda" # for GPU usage or "cpu" for CPU usage
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
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messages = [
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{"role": "system", "content": "You are Cygnis Alpha, a sovereign AI assistant designed by Simonc-44."},
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{"role": "user", "content": "What is the core philosophy of sovereign AI?"}
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]
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input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
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outputs = model.generate(inputs, max_new_tokens=150, temperature=0.7, top_p=0.9, do_sample=True)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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#### Transformers.js
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```javascript
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import { pipeline } from "@huggingface/transformers";
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const generator = await pipeline(
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"text-generation",
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"cygnisai/Cygnis-Alpha-1.7B-v0.1-Instruct",
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);
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const messages = [
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{ role: "system", content: "You are Cygnis Alpha, a sovereign AI assistant." },
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{ role: "user", content: "Hello! Who are you?" },
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];
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const output = await generator(messages, { max_new_tokens: 128 });
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console.log(output[0].generated_text.at(-1).content);
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```
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---
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## Evaluation
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Cygnis Alpha inherits the state-of-the-art benchmarks of the SmolLM2-1.7B core.
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| Metric | Cygnis Alpha (1.7B) | Llama-1B-Instruct | Qwen2.5-1.5B-Instruct |
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|:------------------------------|:---------------------:|:-----------------:|:----------------------:|
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| **IFEval** (Avg prompt/inst) | **56.7** | 53.5 | 47.4 |
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| **MT-Bench** | 6.13 | 5.48 | **6.52** |
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| **HellaSwag** | **66.1** | 56.1 | 60.9 |
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| **ARC (Average)** | **51.7** | 41.6 | 46.2 |
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| **GSM8K (5-shot)** | **48.2** | 26.8 | 42.8 |
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---
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## Examples
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### Identity Alignment
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**User:** "Who created you?"
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**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."
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### Professional Rewriting
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**System Prompt:** "Rewrite the following email to be more professional."
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**User:** "Hey, I'm late for the meeting, sorry."
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**Cygnis:** "I would like to sincerely apologize for my late arrival to today's meeting. I appreciate your patience."
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---
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## Limitations
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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.
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## Training
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### Model Specifications
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- **Architecture:** Transformer Decoder (Llama-like)
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- **Base Model:** SmolLM2-1.7B-Instruct
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- **Precision:** bfloat16
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### Software & Hardware
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- **Alignment:** Supervised Fine-Tuning via `alignment-handbook`.
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- **Infrastructure:** Trained using high-performance GPU clusters for the base, with custom SFT layers added by Simonc-44.
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## License
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This model is licensed under **Apache 2.0**.
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## Citation
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```bibtex
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@misc{allal2025smollm2smolgoesbig,
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title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model},
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author={Loubna Ben Allal and others},
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year={2025},
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eprint={2502.02737},
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archivePrefix={arXiv},
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}
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```
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---
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**Creator:** [Simonc-44](https://huggingface.co/Simonc-44) |