Instructions to use tiny-random/raon-1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download config.json from tiny-random/raon-1-mini: direct link, hf CLI and curl.
- Browser
- Download file 1.87 kB
-
https://huggingface.co/tiny-random/raon-1-mini/resolve/main/config.json
- Command line
-
hf download hf://tiny-random/raon-1-mini/config.json
-
curl -L -o config.json https://huggingface.co/tiny-random/raon-1-mini/resolve/main/config.json
1.87 kB
| { | |
| "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 | |
| } | |