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Model: Madras1/Jade-20B Source: Original Platform
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README.md
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README.md
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---
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language:
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- pt
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- en
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license: apache-2.0
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base_model:
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- unsloth/gpt-oss-20b
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- openai/gpt-oss-20b
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base_model_relation: finetune
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- pt-br
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- portuguese
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- brazilian-portuguese
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- conversational
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- chatbot
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- persona
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- unsloth
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- 4-bit
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- bitsandbytes
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- qwen3
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---
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# Jade-20b
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Jade-20b is a Brazilian Portuguese conversational finetune of gpt-oss-20b 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.
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## Model Summary
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Jade-20b 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.
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## Model Details
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- Developed by: `Madras1`
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- Base model: `unsloth/gpt-oss-20b`
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- Model type: conversational text-generation finetune
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- Primary language: Brazilian Portuguese (`pt-BR`)
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- License: `apache-2.0`
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## Intended Behavior
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This model was trained to:
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- speak naturally in Brazilian Portuguese
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- maintain a consistent Jade persona
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- sound informal, friendly, and chat-oriented
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- work well in casual assistant and conversational use cases
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Typical behavior includes:
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- abbreviations like `vc`
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- light slang and colloquial wording
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- short expressions such as `tmj`, `mano`, `tlgd`
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- a more human and less robotic tone
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If Jade already sounds like a recurring character during inference, that is expected behavior, not an error.
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## Training Intent
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The finetune objective was to make the persona live in the **weights**, not only in prompting.
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High-level training approach:
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- synthetic PT-BR prompt generation for chat-like situations
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- persona-driven response distillation
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- supervised finetuning on conversational data
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- removal of `system` persona instructions during SFT so the model directly internalizes the Jade style
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This is why the model can already answer with personality, abbreviations, and slang even with a simple user-only prompt.
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## Training Setup
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High-level setup used for this finetune:
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- around `25,000` examples
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- `3` epochs
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- Unsloth-based SFT pipeline
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- chat-style data in Portuguese
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## Recommended Use
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Best fit:
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- PT-BR chat assistants
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- persona bots
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- WhatsApp-style conversational agents
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- lightweight entertainment or social AI experiences
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Less ideal for:
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- formal writing
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- highly neutral assistant behavior
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- high-stakes legal, medical, or financial contexts
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## Prompting Tips
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For the strongest Jade behavior:
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- use a simple user message
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- avoid a formal system prompt that fights the finetune
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- keep prompts conversational when possible
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Example prompts:
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- `oi jade, tudo bem?`
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- `jade, me explica isso de um jeito simples`
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- `vc acha que vale a pena estudar python hoje?`
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## Example Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Madras1/Jade-20b"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "user", "content": "oi jade, tudo bem?"}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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temperature=0.7,
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top_p=0.9,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Limitations
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Because this is a persona-oriented finetune:
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- it may sound informal in contexts where a neutral tone would be better
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- it may over-index on chat style depending on the prompt
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- it is optimized more for persona consistency than strict formality
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## Links
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https://github.com/MadrasLe/JadeLLMV-1
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