Instructions to use netto87/dcubelanguage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use netto87/dcubelanguage 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 netto87/dcubelanguage
# Run inference directly in the terminal:
llama cli -hf netto87/dcubelanguage

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf netto87/dcubelanguage
# Run inference directly in the terminal:
llama cli -hf netto87/dcubelanguage

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 netto87/dcubelanguage
# Run inference directly in the terminal:
./llama-cli -hf netto87/dcubelanguage

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 netto87/dcubelanguage
# Run inference directly in the terminal:
./build/bin/llama-cli -hf netto87/dcubelanguage

Use Docker

docker model run hf.co/netto87/dcubelanguage

LM Studio
Jan

vLLM

How to use netto87/dcubelanguage with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "netto87/dcubelanguage"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "netto87/dcubelanguage",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/netto87/dcubelanguage

Ollama
How to use netto87/dcubelanguage with Ollama:
```
ollama run hf.co/netto87/dcubelanguage
```

Unsloth Studio

How to use netto87/dcubelanguage 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 netto87/dcubelanguage 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 netto87/dcubelanguage to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for netto87/dcubelanguage to start chatting

How to use netto87/dcubelanguage with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf netto87/dcubelanguage

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "netto87/dcubelanguage"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use netto87/dcubelanguage with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf netto87/dcubelanguage

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "netto87/dcubelanguage" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use netto87/dcubelanguage with Docker Model Runner:
```
docker model run hf.co/netto87/dcubelanguage
```

Lemonade

How to use netto87/dcubelanguage with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull netto87/dcubelanguage

Run and chat with the model

lemonade run user.dcubelanguage-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use netto87/dcubelanguage with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf netto87/dcubelanguage

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default netto87/dcubelanguage

Run Hermes

hermes

Atomic Chat

DCUBE Language Identifier

Fine-tuned Qwen2.5-0.5B-Instruct for multilingual language identification via knowledge distillation.

Usage

Paste any text — the embedded chat template automatically:

Applies the DCUBE system prompt
Wraps your input with Identify the language:

LM Studio

Download the GGUF file (or convert from this repo)
Load the model — no manual system prompt needed if the GGUF includes the chat template
Paste text directly, e.g. Обичам да слушам музика вечер
Set Temperature = 0

Python (llama-cpp)

py run_gguf.py "Your text here"

Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("YOUR_REPO_ID", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("YOUR_REPO_ID", trust_remote_code=True)

messages = [{"role": "user", "content": "Le temps est magnifique."}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Template auto-adds system prompt + "Identify the language:" prefix

Supported languages

Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Italian, Japanese, Dutch, Polish, Portuguese, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, Chinese, Malayalam

System prompt (embedded in chat template)

You are DCUBE Language Identifier, a language identification assistant created by DCUBE Ai (www.dcubeai.com). Given a text, respond with ONLY the language name. Do not include any explanation.

Training

Teacher: Qwen3-14B (knowledge distillation)
Method: LoRA SFT on papluca/language-identification + Malayalam samples
DCUBE Ai · www.dcubeai.com

Downloads last month: 294

Safetensors

Model size

0.5B params

Tensor type

F16

Model tree for netto87/dcubelanguage

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

(262)

this model