Instructions to use marioparreno/emojify-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marioparreno/emojify-dpo with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="marioparreno/emojify-dpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("marioparreno/emojify-dpo")
model = AutoModelForCausalLM.from_pretrained("marioparreno/emojify-dpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use marioparreno/emojify-dpo with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use marioparreno/emojify-dpo with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="marioparreno/emojify-dpo",
	filename="emojify-dpo.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use marioparreno/emojify-dpo with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf marioparreno/emojify-dpo:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf marioparreno/emojify-dpo:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf marioparreno/emojify-dpo:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf marioparreno/emojify-dpo:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf marioparreno/emojify-dpo:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf marioparreno/emojify-dpo:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf marioparreno/emojify-dpo:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf marioparreno/emojify-dpo:Q4_K_M

Use Docker

docker model run hf.co/marioparreno/emojify-dpo:Q4_K_M

LM Studio
Jan

vLLM

How to use marioparreno/emojify-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marioparreno/emojify-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marioparreno/emojify-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/marioparreno/emojify-dpo:Q4_K_M

SGLang

How to use marioparreno/emojify-dpo with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "marioparreno/emojify-dpo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marioparreno/emojify-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "marioparreno/emojify-dpo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marioparreno/emojify-dpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use marioparreno/emojify-dpo with Ollama:
```
ollama run hf.co/marioparreno/emojify-dpo:Q4_K_M
```

Unsloth Studio new

How to use marioparreno/emojify-dpo with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for marioparreno/emojify-dpo to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for marioparreno/emojify-dpo to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for marioparreno/emojify-dpo to start chatting

Docker Model Runner
How to use marioparreno/emojify-dpo with Docker Model Runner:
```
docker model run hf.co/marioparreno/emojify-dpo:Q4_K_M
```

Lemonade

How to use marioparreno/emojify-dpo with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull marioparreno/emojify-dpo:Q4_K_M

Run and chat with the model

lemonade run user.emojify-dpo-Q4_K_M

List all available models

lemonade list

emojify-dpo

This model is a DPO (Direct Preference Optimization) fine-tuned version of marioparreno/emojify-sft for emojify conversion. It has been optimized to prefer high-quality, semantically accurate emojifications.

Model Description

This model further refines an SFT model by training on preference pairs. For each prompt, the model was shown a "chosen" (preferred) response and a "rejected" response, learning to align its outputs with human (or superior LLM) preferences for emojify conversion.

Training Details

Base Model

Model: marioparreno/emojify-sft
Architecture: Causal LM
Context Length: 256 tokens

LoRA Configuration

LoRA Rank (r): 16
LoRA Alpha: 16
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: 2
Batch Size (per device): 8
Gradient Accumulation Steps: 1
Effective Batch Size: 8
Learning Rate: 3e-06
DPO Beta: 0.1
Max Length: 256
Max Prompt Length: 512
Optimizer: adamw_8bit
Weight Decay: 0.01
Warmup Ratio: 0.1
LR Scheduler: linear
Training Method: Direct Preference Optimization (DPO)
Gradient Checkpointing: unsloth
Training Random Seed: 3407
Random State (Model Init): 3407

Training Results

Total Training Steps: 450
Final Training Loss: 0.4935
Rewards / Chosen: -1.2774
Rewards / Rejected: -2.5320
Reward Accuracy: 0.8750
Reward Margin: 1.2546

Dataset

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

Dataset Statistics

Total Training Examples: 1800
Total Test Examples: 200

Usage

from unsloth import FastModel

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

# Inference
inputs = tokenizer.apply_chat_template(
    [
        {"role": "system", "content": "Translate this text to emoji:"},
        {"role": "user", "content": "I love coding with AI!"},
    ],
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to("cuda")

outputs = model.generate(input_ids=inputs, max_new_tokens=64)
response = tokenizer.batch_decode(outputs)