πŸ”₯ PyroNet-mini

PyroNet-mini is a fine-tuned lightweight variant of microsoft/Phi-4-mini-instruct,
trained with a custom multilingual instruction dataset (English, Ukrainian, Russian).
It belongs to the PyroNet family, serving as the mini version of the main PyroNet.


✨ Features

  • πŸ”Ή Based on Phi-4-mini-instruct
  • πŸ”Ή Fine-tuned with LoRA adapters
  • πŸ”Ή Multilingual (English / Π£ΠΊΡ€Π°Ρ—Π½ΡΡŒΠΊΠ° / Русский)
  • πŸ”Ή Softer safety alignment for more natural answers
  • πŸ”Ή Optimized for dialogue, coding help, and educational purposes

πŸš€ Usage

Inference with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Kenan023214/PyroNet-mini"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Explain quantum entanglement in simple words."}
]


input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

# Generate the response
outputs = model.generate(
    inputs,
    max_new_tokens=256,
    pad_token_id=tokenizer.eos_token_id
)

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

πŸ“Š Intended Use

Educational Q&A

Coding assistance

Multilingual chatbots

Creative writing and storytelling


⚑ Limitations

Being a mini model, it is less capable than larger PyroNet variants

May produce factual inaccuracies

Softer censorship may allow sensitive content


πŸ‘€ Author & Contact

Author: IceL1ghtning

Maintainer: KenanAI

Contact:

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