Instructions to use willt-dc/Rosa-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use willt-dc/Rosa-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="willt-dc/Rosa-V1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("willt-dc/Rosa-V1") model = AutoModelForSequenceClassification.from_pretrained("willt-dc/Rosa-V1") - llama-cpp-python
How to use willt-dc/Rosa-V1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="willt-dc/Rosa-V1", filename="quantized/Rosa-V1-fp16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use willt-dc/Rosa-V1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf willt-dc/Rosa-V1:Q6_K # Run inference directly in the terminal: llama-cli -hf willt-dc/Rosa-V1:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf willt-dc/Rosa-V1:Q6_K # Run inference directly in the terminal: llama-cli -hf willt-dc/Rosa-V1:Q6_K
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 willt-dc/Rosa-V1:Q6_K # Run inference directly in the terminal: ./llama-cli -hf willt-dc/Rosa-V1:Q6_K
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 willt-dc/Rosa-V1:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf willt-dc/Rosa-V1:Q6_K
Use Docker
docker model run hf.co/willt-dc/Rosa-V1:Q6_K
- LM Studio
- Jan
- Ollama
How to use willt-dc/Rosa-V1 with Ollama:
ollama run hf.co/willt-dc/Rosa-V1:Q6_K
- Unsloth Studio new
How to use willt-dc/Rosa-V1 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 willt-dc/Rosa-V1 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 willt-dc/Rosa-V1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for willt-dc/Rosa-V1 to start chatting
- Docker Model Runner
How to use willt-dc/Rosa-V1 with Docker Model Runner:
docker model run hf.co/willt-dc/Rosa-V1:Q6_K
- Lemonade
How to use willt-dc/Rosa-V1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull willt-dc/Rosa-V1:Q6_K
Run and chat with the model
lemonade run user.Rosa-V1-Q6_K
List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf willt-dc/Rosa-V1:Q6_K# Run inference directly in the terminal:
llama-cli -hf willt-dc/Rosa-V1:Q6_KUse 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 willt-dc/Rosa-V1:Q6_K# Run inference directly in the terminal:
./llama-cli -hf willt-dc/Rosa-V1:Q6_KBuild 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 willt-dc/Rosa-V1:Q6_K# Run inference directly in the terminal:
./build/bin/llama-cli -hf willt-dc/Rosa-V1:Q6_KUse Docker
docker model run hf.co/willt-dc/Rosa-V1:Q6_KROSA :: Emotional Sensitivity
“To feel is to know; to know is to bloom.” ·Willinton
ROSA is a fine-tuned Transformer model based on bert-base-uncased, trained on the GoEmotions dataset to classify 28 nuanced human emotions (plus neutral).
More than a model, ROSA is a prototype of emotion embeddings in affective computing.
🧠 Model Summary
| Metric | Value |
|---|---|
| Eval Loss | 0.0845 |
| Eval F1 | 0.5793 |
| Epochs | 3 |
| Dataset | GoEmotions |
| Model Base | BERT |
| Parameters | ~110M |
✨ Highlights
- Supports multilabel emotion classification
- Returns soft probability scores for each of the 29 emotions
- Includes optional latent vector embedding for downstream affect modeling
- Trained with HuggingFace
Trainer+ early evaluation - Symbolically aligned to human-centered semantics and poetic logic
🌸 Emotion Set
["admiration", "amusement", "anger", "annoyance", "approval", "caring",
"confusion", "curiosity", "desire", "disappointment", "disapproval",
"disgust", "embarrassment", "excitement", "fear", "gratitude", "grief",
"joy", "love", "nervousness", "optimism", "pride", "realization", "relief",
"remorse", "sadness", "surprise", "neutral"]
🔮 Usage
from transformers import BertTokenizer
from model.emotion_model import Rosa
import torch
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = Rosa(num_emotions=29)
model.load_state_dict(torch.load("rosa.pt"))
model.eval()
text = "My heart is filled with longing and beauty."
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.sigmoid(outputs["logits"]).squeeze()
# Result: list of probabilities for each emotion
🧭 Confusion Matrix
Included in the assets/ directory as confusion_matrix.png to show classification precision across emotions.
🧩 Architecture
┌──────────────┐
│ BERT Encoder │
└──────┬───────┘
↓
┌─────────────────┐
│ Dropout (Grace) │
└─────────────────┘
↓
┌────────────────────────┐
│ Dense Output (Bloom) │ → logits over 29 emotions
└────────────────────────┘
📦 Installation
pip install -r requirements.txt
Includes:
transformerstorchdatasetsscikit-learn
🖋️ License
CreativeML Open RAIL-M License
Please use this model ethically and with reverence for emotional contexts.
🌹 Creator
Willinton Triana Cardona
Philosopher · AI Engineer · Architect of Poetic Systems
ROSA is the Rosa of Barcelona, my first blossom of affective computing, semantic elegance, and sacred recursion.
🤝 Contributing
Pull requests, poetic expansions, multilingual emotion embeddings, and related metaphoric augmentations are welcome. I promise the next iteration (v2 with F1 improved) soon
📍Hugging Face Hub
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf willt-dc/Rosa-V1:Q6_K# Run inference directly in the terminal: llama-cli -hf willt-dc/Rosa-V1:Q6_K