Instructions to use tiny-random/raon-1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tiny-random/raon-1-mini with Transformers:

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

pipe = pipeline("text-generation", model="tiny-random/raon-1-mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("tiny-random/raon-1-mini", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tiny-random/raon-1-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tiny-random/raon-1-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tiny-random/raon-1-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tiny-random/raon-1-mini

SGLang

How to use tiny-random/raon-1-mini 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 "tiny-random/raon-1-mini" \
    --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": "tiny-random/raon-1-mini",
		"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 "tiny-random/raon-1-mini" \
        --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": "tiny-random/raon-1-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tiny-random/raon-1-mini with Docker Model Runner:
```
docker model run hf.co/tiny-random/raon-1-mini
```

raon-1-mini

File size: 1,865 Bytes

{
  "architectures": [
    "Raon1MiniForCausalLM"
  ],
  "model_type": "raon1_mini",
  "dtype": "bfloat16",
  "hidden_size": 32,
  "num_hidden_layers": 8,
  "num_attention_heads": 1,
  "head_dim": 32,
  "vocab_size": 259840,
  "tie_word_embeddings": false,
  "max_position_embeddings": 4096,
  "rms_norm_eps": 1e-05,
  "rope_parameters": null,
  "hidden_act": "situ",
  "activation_situ_beta": 4.0,
  "activation_situ_linear_beta": 25.0,
  "intermediate_size": 128,
  "first_k_dense_replace": 1,
  "moe_layer_freq": 1,
  "num_experts": 64,
  "num_experts_per_token": 6,
  "moe_intermediate_size": 32,
  "num_shared_experts": 1,
  "moe_router_activation_func": "sigmoid",
  "moe_renormalize": true,
  "routed_scaling_factor": 1.0,
  "q_lora_rank": 32,
  "kv_lora_rank": 32,
  "qk_nope_head_dim": 32,
  "qk_rope_head_dim": 32,
  "v_head_dim": 32,
  "mla_use_nope": true,
  "mla_use_output_gate": true,
  "raon_mhc_streams": 4,
  "rda_config": {
    "num_heads": 1,
    "head_dim": 32,
    "conv_kernel_size": 4,
    "full_rank_gate": true,
    "gate_lower_bound": -5.0,
    "mla_layers": [
      4,
      8
    ],
    "rda_layers": [
      1,
      2,
      3,
      5,
      6,
      7
    ]
  },
  "auto_map": {
    "AutoConfig": "configuration_raon1_mini.Raon1MiniConfig",
    "AutoModel": "modeling_raon1_mini.Raon1MiniModel",
    "AutoModelForCausalLM": "modeling_raon1_mini.Raon1MiniForCausalLM"
  },
  "quantization_config": {
    "quant_method": "mxfp8",
    "activation_scheme": "dynamic",
    "weight_block_size": [
      1,
      32
    ],
    "ignored_layers": [
      "lm_head",
      "model.layers.*.self_attn*",
      "model.layers.*.mlp*",
      "model.layers.*.moe.gate*",
      "model.layers.*.moe.shared_experts*",
      "model.layers.*.moe.experts$"
    ],
    "dequantize": false
  },
  "transformers_version": "5.16.1",
  "rda_decay_rank": 32
}