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

dcubelanguage

File size: 2,010 Bytes

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---

license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
  - language-identification
  - knowledge-distillation
  - dcube
  - qwen2
pipeline_tag: text-generation
---


# 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:
1. Applies the DCUBE system prompt
2. Wraps your input with `Identify the language:`

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

### Python (llama-cpp)
```bash

py run_gguf.py "Your text here"

```

### Hugging Face Transformers
```python

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