Instructions to use baruus/arabic-intent-telecom-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use baruus/arabic-intent-telecom-llm with PEFT:
```
Task type is invalid.
```

How to use baruus/arabic-intent-telecom-llm with Transformers:

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

pipe = pipeline("text-generation", model="baruus/arabic-intent-telecom-llm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("baruus/arabic-intent-telecom-llm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use baruus/arabic-intent-telecom-llm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/baruus/arabic-intent-telecom-llm

SGLang

How to use baruus/arabic-intent-telecom-llm 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 "baruus/arabic-intent-telecom-llm" \
    --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": "baruus/arabic-intent-telecom-llm",
		"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 "baruus/arabic-intent-telecom-llm" \
        --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": "baruus/arabic-intent-telecom-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use baruus/arabic-intent-telecom-llm with Docker Model Runner:
```
docker model run hf.co/baruus/arabic-intent-telecom-llm
```

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

Configuration Parsing Warning:Config file adapter_config.json cannot be fetched (too big)

Arabic Intent Telecom LLM

This model is a fine-tuned Arabic language model designed to extract user intent and classify telecom service requests into network slice categories (network_slice_high or network_slice_low). It powers a YAML policy generator for telecom automation workflows.

🔧 Model Card for `intent_model_output`

This model is a fine-tuned version of unsloth/qwen2-7b-instruct-bnb-4bit.
It was trained using TRL and the PEFT LoRA adapter method.

🚀 Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model = AutoModelForCausalLM.from_pretrained("baruus/arabic-intent-telecom-llm", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("baruus/arabic-intent-telecom-llm")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "حابب شريحة إنترنت للتحميل السريع لو سمحت"
output = pipe(prompt, max_new_tokens=100)[0]["generated_text"]
print(output)

Downloads last month: 11

Arabic Intent Telecom LLM

🔧 Model Card for intent_model_output

🚀 Quick Start

🔧 Model Card for `intent_model_output`