README.md

---
language:
- pt
- en
license: apache-2.0
base_model: unsloth/qwen3-4b
base_model_relation: finetune
library_name: transformers
pipeline_tag: text-generation
tags:
- pt-br
- portuguese
- brazilian-portuguese
- conversational
- chatbot
- persona
- qwen2
- qwen2.5
- unsloth
- 4-bit
- bitsandbytes
---

# 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

```python
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