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Model: Madras1/Jade1.7b Source: Original Platform
2026-04-30 18:57:42 +08:00
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--- a/README.md
+++ b/README.md
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+---
+language:
+- pt
+- en
+license: apache-2.0
+base_model:
+- unsloth/qwen3-1.7b
+- Qwen/Qwen3-1.7B
+base_model_relation: finetune
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- pt-br
+- portuguese
+- brazilian-portuguese
+- conversational
+- chatbot
+- persona
+- unsloth
+- 4-bit
+- bitsandbytes
+- qwen3
+---
+![Total Downloads All Time](https://img.shields.io/badge/dynamic/json?color=brightgreen&label=Total%20Downloads&query=%24.downloadsAllTime&url=https%3A%2F%2Fhuggingface.co%2Fapi%2Fmodels%2FMadras1%2FJade1.7b%3Fexpand%3DdownloadsAllTime)
+
+# Jade1.7b
+
+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
--- a/added_tokens.json
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--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user
+' + message['content'] + '<|im_end|>
+'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant
+' + message['content'] + '<|im_end|>
+' }}{% else %}{{ '<|im_start|>system
+' + message['content'] + '<|im_end|>
+' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
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+  "attention_dropout": 0.0,
+  "dtype": "bfloat16",
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+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 6144,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
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+  "max_window_layers": 28,
+  "model_type": "qwen3",
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+}
--- a/generation_config.json
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+}
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--- a/vocab.json
+++ b/vocab.json