GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.

You can find more info about me and what I do here.

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

python

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

yaml

# 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

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

Flexan/marioparreno-emojify-sft-GGUF

GGUF Files for emojify-sft

Downloads

Note from Flexan

marioparreno/emojify-sft

Model Description

Training Details

Base Model

LoRA Configuration

Quantization

Training Hyperparameters

Training Results

Training Monitoring

Dataset

Dataset Statistics

Example Predictions

Example Predictions

Usage

Training Configuration

Model Card Authors

Flexan/
marioparreno-emojify-sft-GGUF