Instructions to use willt-dc/Rosa-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use willt-dc/Rosa-V1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

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

Atomic Chat

willt-dc commited on Jul 31, 2025

Commit

90d61a2

verified ·

1 Parent(s): 3399e8d

Update quantized/README.md

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@@ -1,44 +1,4 @@
----
-base_model: willt-dc/Rosa-V1
-datasets:
-- go_emotions
-  language: en
-  library_name: transformers
-  license: creativeml-openrail-m
-  model_creator: Willinton Triana Cardona
-  model_name: Rosa-V1
-  model_type: bert
-  quantized_by: Willinton Triana Cardona
-  quantized_models:
-- name: Rosa-V1-fp16
-  path: quantized/Rosa-V1-fp16.gguf
-  format: GGUF
-  description: Full-precision baseline
-- name: Rosa-V1-q8_0
-  path: quantized/Rosa-V1-q8_0.gguf
-  format: GGUF
-  description: Near-lossless 8-bit quantization
-- name: Rosa-V1-q6_k
-  path: quantized/Rosa-V1-q6_k.gguf
-  format: GGUF
-  description: Balanced 6-bit quantization for portability
-  tags:
-- emotion-classification
-- multilabel
-- bert
-- goemotions
-- affective-computing
-- psychology
-- NLP
-- embeddings
-- symbolic-ai
-- poetic-ai
-- quantized
-- gguf
----
 # Rosa V1 — Fidelity Edition (GGUF)



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