Text Generation
Transformers
Safetensors
taonet
trust-remote-code
sentencepiece
custom-architecture
custom_code
Instructions to use TaoTern/TaoNet-mini-A2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TaoTern/TaoNet-mini-A2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TaoTern/TaoNet-mini-A2", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TaoTern/TaoNet-mini-A2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TaoTern/TaoNet-mini-A2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TaoTern/TaoNet-mini-A2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TaoTern/TaoNet-mini-A2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TaoTern/TaoNet-mini-A2
- SGLang
How to use TaoTern/TaoNet-mini-A2 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 "TaoTern/TaoNet-mini-A2" \ --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": "TaoTern/TaoNet-mini-A2", "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 "TaoTern/TaoNet-mini-A2" \ --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": "TaoTern/TaoNet-mini-A2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TaoTern/TaoNet-mini-A2 with Docker Model Runner:
docker model run hf.co/TaoTern/TaoNet-mini-A2
File size: 1,502 Bytes
fd448dd 32d5d5f fd448dd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | {
"architectures": [
"TaoNetForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_taonet.TaoNetConfig",
"AutoModelForCausalLM": "modeling_taonet.TaoNetForCausalLM"
},
"bos_token_id": 1,
"cnn_channels": [
32,
64,
128
],
"cnn_kernel_size": 3,
"d_embed_rank": 96,
"d_latent_kv": 768,
"d_rope": 128,
"dropout": 0.02,
"dtype": "float32",
"eos_token_id": 2,
"gamma_activation": "gelu",
"gamma_discretization": "bilinear",
"gamma_dt_init": 0.01,
"gamma_dt_max": 0.1,
"gamma_dt_min": 0.001,
"gamma_gate": true,
"gamma_gate_bias": 2.0,
"gamma_hidden_dim": 1536,
"gamma_input_gate": true,
"gamma_input_gate_bias": 2.0,
"gamma_kernel_mode": "auto",
"gamma_kernel_threshold": 64,
"gamma_layer_scale_init": 0.1,
"gamma_prenorm": true,
"gamma_residual_scale": 1.0,
"gamma_use_D": true,
"gamma_use_output_linear": true,
"gqa_groups": 1,
"head_dim": 128,
"hidden_dim": 1024,
"hidden_dim_ff": 3072,
"image_size": 224,
"image_token": "<image>",
"init_std": 0.02,
"intermediate_dim": 4096,
"max_seq_length": 1024,
"model_type": "taonet",
"num_heads": 8,
"num_layers": 16,
"pad_token_id": 3,
"rope_scale": 40.0,
"transformers_version": "5.1.0",
"unk_token_id": 0,
"use_factorized_embedding": true,
"vision_encoder_type": "cnn",
"vision_output_dim": 256,
"vision_prefix_tokens": 10,
"vocab_size": 8192,
"yarn_alpha": 1.0,
"yarn_enabled": false,
"yarn_original_max_seq_length": null
}
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