Jade4b

Jade4b

Jade4b is a Brazilian Portuguese conversational finetune of Qwen3 4b built to express a strong, persistent persona. This model is designed for PT-BR chat, chatbot use cases, and character-style interaction, with colloquial language, abbreviations, slang, and a WhatsApp-like tone.

Model Summary

Jade4b is a persona-first model. It was intentionally finetuned so the model speaks like Jade even without a strong system prompt. Because of that, the model often answers in PT-BR with informal phrasing such as vc, slang, and a friendly conversational tone from the very first turn.

Model Details

• Developed by: Madras1
• Base model: unsloth/qwen3-4b
• Model type: conversational text-generation finetune
• Primary language: Brazilian Portuguese (pt-BR)
• License: apache-2.0

Intended Behavior

This model was trained to:

• speak naturally in Brazilian Portuguese
• maintain a consistent Jade persona
• sound informal, friendly, and chat-oriented
• work well in casual assistant and conversational use cases

Typical behavior includes:

• abbreviations like vc
• light slang and colloquial wording
• short expressions such as tmj, mano, tlgd
• a more human and less robotic tone

If Jade already sounds like a recurring character during inference, that is expected behavior, not an error.

Training Intent

The finetune objective was to make the persona live in the weights, not only in prompting.

High-level training approach:

• synthetic PT-BR prompt generation for chat-like situations
• persona-driven response distillation
• supervised finetuning on conversational data
• removal of system persona instructions during SFT so the model directly internalizes the Jade style

This is why the model can already answer with personality, abbreviations, and slang even with a simple user-only prompt.

Training Setup

High-level setup used for this finetune:

• around 25,000 examples
• 3 epochs
• Unsloth-based SFT pipeline
• chat-style data in Portuguese

Recommended Use

Best fit:

• PT-BR chat assistants
• persona bots
• WhatsApp-style conversational agents
• lightweight entertainment or social AI experiences

Less ideal for:

• formal writing
• highly neutral assistant behavior
• high-stakes legal, medical, or financial contexts

Prompting Tips

For the strongest Jade behavior:

• use a simple user message
• avoid a formal system prompt that fights the finetune
• keep prompts conversational when possible

Example prompts:

• oi jade, tudo bem?
• jade, me explica isso de um jeito simples
• vc acha que vale a pena estudar python hoje?

Example Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Madras1/Jade4b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "oi jade, tudo bem?"}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
)

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

Limitations

Because this is a persona-oriented finetune:

• it may sound informal in contexts where a neutral tone would be better
• it may over-index on chat style depending on the prompt
• it is optimized more for persona consistency than strict formality