---
license: apache-2.0
base_model: unsloth/gemma-3-4b-it
tags:
- unsloth
- gemma-3
- sft
- cpt
- paite
language:
- pck
- en
pipeline_tag: text-generation
---

# Sensix Paite 4B Instruction (16-bit)

This model is a fine-tuned version of Gemma 3 4B developed through a two-stage training pipeline: Continued Pre-Training (CPT) for vocabulary acquisition and Supervised Fine-Tuning (SFT) for instruction following. It is optimized for native Paite linguistic reasoning.

## Model Details
- **Base Model:** unsloth/gemma-3-4b-it
- **Target Language:** Paite (pck)
- **Precision:** 16-bit bfloat16 (Full Precision)
- **Training Method:** LoRA with Hard Merge
- **Framework:** Unsloth, TRL, and PEFT

## Training Procedure

### Stage 1: Continued Pre-Training (CPT)
Knowledge injection was performed using the `PERFECT_PAITE_DATA.jsonl` dataset (articles, news, and long-form paragraphs).
- **Learning Rate:** 2e-4
- **LoRA Config:** r=64, alpha=128
- **Focus:** Modern Paite vocabulary expansion, excluding repetitive scriptural fragments.

### Stage 2: Supervised Fine-Tuning (SFT)
The model was refined on `mixed_alpaca_paite_2026-04-09.jsonl` to establish instruction-following logic.
- **Learning Rate:** 2e-5
- **Epochs:** 3
- **Data Packing:** Enabled
- **Prompt Format:** Gemma 3 Chat Template (Messaging Format)

## Technical Implementation: Hard Merge Strategy
To prevent the common weight-scrambling issue (known as the "Calcium/Blades" gibberish bug) found in Gemma 3/4 merges, this model was fused using an official Hard Merge (PEFT `merge_and_unload`) rather than simple weight averaging. This ensures 100% stability and preserves the model's reasoning logic.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "sensix-zo/sensix-paite-4b-instruction-16bit"

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

# Example Prompt
messages = [
    {"role": "user", "content": "Paite pau hi bangchiah in a poimoh hiam?"}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

Limitations

This model is optimized for the Paite language. While it retains English capabilities, users should verify complex technical outputs for accuracy.