marioparreno-emojify-sft-GGUF

GGUF Files for emojify-sft

These are the GGUF files for marioparreno/emojify-sft.

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marioparreno/emojify-sft

This model is a fine-tuned version of unsloth/gemma-3-270m-it for emojify conversion. It was trained using LoRA (Low-Rank Adaptation) with the unsloth library for efficient fine-tuning.

Model Description

This model converts natural language text into emoji representations, learning to identify the most appropriate emojis that capture the semantic meaning and emotional content of the input text.

Training Details

Base Model

• Model: unsloth/gemma-3-270m-it
• Architecture: Gemma-3
• Context Length: 256 tokens

LoRA Configuration

• LoRA Rank (r): 16
• LoRA Alpha: 32
• LoRA Dropout: 0.0
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Quantization

• 4-bit Quantization: True
• 8-bit Quantization: False

Training Hyperparameters

• Training Epochs: 3
• Batch Size (per device): 8
• Gradient Accumulation Steps: 1
• Effective Batch Size: 8
• Learning Rate: 5e-05
• Optimizer: adamw_8bit
• Weight Decay: 0.01
• Warmup Steps: 5
• LR Scheduler: linear
• Training Method: Supervised Fine-Tuning (SFT) with train_on_responses_only
• Gradient Checkpointing: unsloth
• Training Random Seed: 3407
• Random State (Model Init): 3407

Training Results

• Total Training Steps: 759
• Final Training Loss: 2.1543
• Final Emoji Accuracy: 91.09%
• Emoji-Only Predictions: 460 / 505

Training Monitoring

Training was monitored using Weights & Biases:

• W&B Run: 2clqnn7j
• View training curves and metrics: Dashboard

Dataset

This model was trained on the marioparreno/emojify-sft dataset.

Dataset Statistics

• Total Training Examples: 2,023
• Total Test Examples: 505
• Total Examples: 2,528
• Dataset Version: 1b1ee9e
• Last Modified: 2026-02-25
• Full Commit SHA: 1b1ee9efd92f1dbba4b3141e53b97e0d466981ba

Example Predictions

The following examples show the model's predictions on the test set:

Example Predictions

Example predictions were logged to Weights & Biases during training. Please view the training run for detailed examples. To see prediction examples, visit the W&B dashboard linked above and check the "eval/examples" table.

Usage

from unsloth import FastModel
from unsloth.chat_templates import get_chat_template

# Load the fine-tuned model
model, tokenizer = FastModel.from_pretrained(
    model_name="marioparreno/emojify-sft",
    max_seq_length=256,
    load_in_4bit=True,
)

# Setup chat template
tokenizer = get_chat_template(
    tokenizer,
    chat_template="gemma3",
)

# Prepare input
messages = [
    {"role": "system", "content": "Translate this text to emoji:"},
    {"role": "user", "content": "I love programming in Python!"}
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to("cuda")

# Generate
outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=32,
    temperature=1.0,
    top_p=0.95,
    top_k=64,
)

# Decode
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Configuration

# Chat Template Parts
instruction_part: "<start_of_turn>user
"
response_part: "<start_of_turn>model
"

# Evaluation
eval_strategy: "steps"
eval_steps: 50
logging_steps: 10

Model Card Authors

Mario Parreño

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This model card was automatically generated as part of the training pipeline.