Text Generation
Transformers
PyTorch
English
moa_metric
mixture-of-attentions
distance-attention
metric-attention
mqa
hyperffn
router-gating
convergentintel
Instructions to use reaperdoesntknow/MoA-100M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reaperdoesntknow/MoA-100M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reaperdoesntknow/MoA-100M")# Load model directly from transformers import MoAMetricLM model = MoAMetricLM.from_pretrained("reaperdoesntknow/MoA-100M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use reaperdoesntknow/MoA-100M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reaperdoesntknow/MoA-100M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reaperdoesntknow/MoA-100M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/reaperdoesntknow/MoA-100M
- SGLang
How to use reaperdoesntknow/MoA-100M 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 "reaperdoesntknow/MoA-100M" \ --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": "reaperdoesntknow/MoA-100M", "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 "reaperdoesntknow/MoA-100M" \ --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": "reaperdoesntknow/MoA-100M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use reaperdoesntknow/MoA-100M with Docker Model Runner:
docker model run hf.co/reaperdoesntknow/MoA-100M
| { | |
| "alpha_init": 1.0, | |
| "architectures": [ | |
| "MoAMetricLM" | |
| ], | |
| "attn_drop": 0.1, | |
| "attn_heads": 32, | |
| "bos_token_id": 101, | |
| "conv_kernel": 5, | |
| "conv_mult": 2, | |
| "dim": 1024, | |
| "discrepancy_modulation": true, | |
| "drop_path": 0.0, | |
| "dtype": "float32", | |
| "enable_feature_gates": true, | |
| "enable_router_gates": true, | |
| "energy_amplification": 0.1, | |
| "eos_token_id": 102, | |
| "ff_mult": 4, | |
| "ffn_hidden": 2048, | |
| "head_feature_heads": 8, | |
| "layer_scale_init_value": 0.0001, | |
| "learn_alpha": true, | |
| "learn_radius": true, | |
| "lr_rank": 32, | |
| "maha_init": 1.0, | |
| "max_position_embeddings": 1024, | |
| "max_seq_len_cached": 8192, | |
| "metric": "l2", | |
| "mixer_hidden": 2048, | |
| "model_type": "moa_metric", | |
| "mqa_q_heads": 64, | |
| "n_branches": 3, | |
| "n_token_router_heads": 4, | |
| "num_hidden_layers": 6, | |
| "num_layers": 6, | |
| "origin_init_scale": 0.0, | |
| "pad_token_id": 0, | |
| "proj_drop": 0.1, | |
| "r_basis": 16, | |
| "radius_init": 3.0, | |
| "router_bias_heads": 4, | |
| "router_dropout": 0.1, | |
| "router_hidden": 2048, | |
| "router_init_temperature": 2.0, | |
| "router_temperature": 1.0, | |
| "router_topk": 2, | |
| "shared_kv_ratio": 0.5, | |
| "theta_base": 10000.0, | |
| "ti_reg_samples": 0, | |
| "ti_reg_weight": 0.0, | |
| "transformers_version": "4.56.1", | |
| "use_balls": true, | |
| "vocab_size": 50257 | |
| } | |