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
```

darkps commited on 5 days ago

Commit

c6cc815

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1 Parent(s): 2736e30

Upload config.json

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config.json +1 -65

config.json CHANGED Viewed

@@ -1,67 +1,3 @@
 {
-    "architectures": [
-        "Qwen3ForCausalLM"
-    ],
-    "attention_bias": false,
-    "attention_dropout": 0.0,
-    "bos_token_id": 151643,
-    "torch_dtype": "bfloat16",
-    "eos_token_id": 151645,
-    "head_dim": 128,
-    "hidden_act": "silu",
-    "hidden_size": 4096,
-    "initializer_range": 0.02,
-    "intermediate_size": 12288,
-    "layer_types": [
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention",
-        "full_attention"
-    ],
-    "max_position_embeddings": 40960,
-    "max_window_layers": 36,
-    "model_type": "qwen3",
-    "num_attention_heads": 32,
-    "num_hidden_layers": 36,
-    "num_key_value_heads": 8,
-    "rms_norm_eps": 1e-06,
-    "rope_scaling": null,
-    "rope_theta": 1000000,
-    "sliding_window": null,
-    "tie_word_embeddings": false,
-    "use_cache": true,
-    "use_sliding_window": false,
-    "vocab_size": 151936
 }

 {
+  "architectures": ["ice-0001"]
 }