DrRiceIO7/Oryza-Sirocco-3B-ARC

I remember this model now! It's really bad! I messed up the parameters when loading the dataset and ended up with a model that just writes the wrong things. Don't use this! I'll keep it around to remind me of how I've progressed from the start.

Old README:

Oryza-Sirocco-3B

⚠️ Warning: Automated Release

This is an automated release. This model has been fine-tuned and merged automatically. It has not been manually tested for quality, coherence, or safety. Use with caution.

Model Details

Oryza-Sirocco-3B is a fine-tuned version of unsloth/Ministral-3-3B-Base-2512. It was trained using the Unsloth library for faster and memory-efficient training.

The primary goal of this fine-tune is to reduce refusals, moralizing, and "spineless" behavior, making the model more direct and responsive to instructions, including those that might be perceived as aggressive or "unsafe"

• Base Model: unsloth/Ministral-3-3B-Base-2512
• Architecture: Ministral 3 (Mistral)
• Framework: Unsloth / Transformers / TRL
• Language: English

Training Data

This model was trained on the following datasets:

1. DrRiceIO7/aggressive-instructions: A dataset where responses are "aggressive", "sarcastic", or otherwise more personality filled than standard LLM responses.
2. DrRiceIO7/AlteredDataset: A dataset combining creative writing, humanized responses, and uncensored instruction following.

Note: Specific details about the dataset contents were not publicly available at the time of generation.

Training Details

The model was trained with the following hyperparameters using SFTTrainer:

• Epochs: 1 (approx. 9375 steps)
• Batch Size: 2 per device (Gradient Accumulation: 8) -> Effective Batch Size: 16
• Learning Rate: 2e-5
• Optimizer: AdamW 8-bit
• LoRA Rank (r): 32
• LoRA Alpha: 64
• Max Sequence Length: 2048

Usage

You can run this model using the transformers library or unsloth.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "DrRiceIO7/Oryza-Sirocco-3B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"},
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)

outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

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Disclaimer: This README was generated by Gemini 3 Pro Preview.