Jade1.7b

Name: Jade1.7b
Availability: InStock

Jade1.7b is a Brazilian Portuguese conversational finetune of Qwen3 1.7b 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

Jade1.7b 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/qwen3-1.7b
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

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Madras1/Jade1.7b"

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

Links

https://github.com/MadrasLe/JadeLLMV-1