Text Generation
Transformers
Safetensors
Vietnamese
English
afmoe
Mixture of Experts
mixture-of-experts
decode-series
llm
vietnamese-llm
Instructions to use Minh2508/Decode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Minh2508/Decode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Minh2508/Decode")# Load model directly from transformers import AutoTokenizer, MOE tokenizer = AutoTokenizer.from_pretrained("Minh2508/Decode") model = MOE.from_pretrained("Minh2508/Decode") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Minh2508/Decode with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Minh2508/Decode" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minh2508/Decode", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Minh2508/Decode
- SGLang
How to use Minh2508/Decode 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 "Minh2508/Decode" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minh2508/Decode", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Minh2508/Decode" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minh2508/Decode", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Minh2508/Decode with Docker Model Runner:
docker model run hf.co/Minh2508/Decode
| { | |
| "model_type": "afmoe", | |
| "architectures": [ | |
| "MOE" | |
| ], | |
| "profile_name": "1b_3e_8l_t4x2", | |
| "vocab_size": 200024, | |
| "text_embed_dim": 1024, | |
| "vision_embed_dim": 1024, | |
| "hidden_dim": 1024, | |
| "ffn_dim": 6144, | |
| "num_layers": 8, | |
| "num_heads": 16, | |
| "num_kv_heads": 4, | |
| "num_experts": 3, | |
| "top_k": 2, | |
| "max_position_embeddings": 16384, | |
| "router_aux_loss_coef": 0.01, | |
| "share_experts_across_layers": false, | |
| "gradient_checkpointing": true, | |
| "num_agents": 4, | |
| "moe_capacity_factor": 1.0, | |
| "moe_hierarchy_groups": 1, | |
| "moe_hierarchy_top_k": 1, | |
| "num_shared_experts": 0, | |
| "load_balancing_mode": "aux_free", | |
| "router_bias_update_rate": 0.01, | |
| "kv_latent_dim": 128, | |
| "kv_cache_dtype": "int4", | |
| "rope_training_context": 16384, | |
| "rope_ntk_alpha": 1.0, | |
| "rope_yarn_scale": 1.0, | |
| "ring_attention_chunk_size": 0, | |
| "prefill_chunk_size": 256, | |
| "use_q_former_projector": true, | |
| "q_former_queries": 8, | |
| "q_former_layers": 1, | |
| "tokenizer_name": "ai-tokenizer:GPT-5" | |
| } |