Instructions to use darkps/ice-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darkps/ice-AI with Transformers:

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

pipe = pipeline("text-generation", model="darkps/ice-AI")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("darkps/ice-AI")
model = AutoModelForCausalLM.from_pretrained("darkps/ice-AI", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use darkps/ice-AI with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/darkps/ice-AI

SGLang

How to use darkps/ice-AI 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 "darkps/ice-AI" \
    --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": "darkps/ice-AI",
		"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 "darkps/ice-AI" \
        --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": "darkps/ice-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darkps/ice-AI with Docker Model Runner:
```
docker model run hf.co/darkps/ice-AI
```

ice-AI / README.md

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	---
	language: multilingual
	license: apache-2.0
	author: DarkPs
	library_name: transformers
	tags:
	- ice-ai
	- ice-0001
	- text-generation
	- conversational
	- programming
	pipeline_tag: text-generation
	---

	<p align="center">
	<img src="ice.jpg" width="280" alt="ICE AI">
	</p>

	# ICE 0001

	The "ice" model is a very robust, medium-sized model for human-like conversations, designed for quick chats and small code snippets.
	---

	The ice model was released with massive equations:

	### Major Improvements

	* It was trained on 5.47 billion Codex conversations.
	* It was also trained on more than 30 billion human conversations to better adapt to different Arabic dialects and multiple languages.

	---

	# Key Specifications

	* Model Family: ICE AI
	* ID: ice-0001
	* Model Size: 8B Parameters
	* Context Length: 32,768 tokens
	* Format: Transformers
	* Inference Support: CPU / GPU
	* Primary Focus: Human-like conversational AI

	### Training

	Trained on approximately 36 trillion tokens across 119 languages and dialects, with a strong focus on multiple Arabic dialects, international languages, and programming/code data.

	---

	# Recommended Usage

	ICE AI performs best when used for:

	* General conversations
	* multilingual chat
	* Software development
	* Code generation
	* Code debugging
	* Technical questions
	* Scripting and automation
	* Local offline AI deployment

	---

	# ⚠️ Notes

	* Designed for conversational and coding tasks.
	* Output quality may vary depending on the quantization level and hardware.
	* Best results are achieved with structured prompts.
	* Larger context sizes may require substantial RAM/VRAM.

	---

	# About Dark

	DarkPs is an AI organization owned by FanuonAI, developing and maintaining open-source AI models such as DarkIT, ICE AI, and DarkCoder.

	Platform: https://dark.ps