Text Generation
Transformers
Safetensors
maccy
custom_code
mixture-of-experts
kimi-delta-attention
multi-head-latent-attention
Instructions to use bgub/maccy-106m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bgub/maccy-106m-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bgub/maccy-106m-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("bgub/maccy-106m-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use bgub/maccy-106m-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bgub/maccy-106m-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bgub/maccy-106m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bgub/maccy-106m-base
- SGLang
How to use bgub/maccy-106m-base 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 "bgub/maccy-106m-base" \ --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": "bgub/maccy-106m-base", "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 "bgub/maccy-106m-base" \ --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": "bgub/maccy-106m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use bgub/maccy-106m-base with Docker Model Runner:
docker model run hf.co/bgub/maccy-106m-base
| { | |
| "architectures": [ | |
| "MaccyForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_maccy.MaccyConfig", | |
| "AutoModelForCausalLM": "modeling_maccy.MaccyForCausalLM" | |
| }, | |
| "bias": false, | |
| "bos_token_id": 32759, | |
| "channel_mixer_pattern": "moe", | |
| "context_length": 1024, | |
| "d_model": 576, | |
| "dtype": "float32", | |
| "eos_token_id": 32763, | |
| "mixer_pattern": "kda,kda,kda,mla", | |
| "mla": { | |
| "content_head_dim": 64, | |
| "gated": false, | |
| "kv_rank": 72, | |
| "query_rank": 144, | |
| "rope_head_dim": 32, | |
| "value_head_dim": 64 | |
| }, | |
| "mlp_expansion": 3, | |
| "model_type": "maccy", | |
| "moe": { | |
| "capacity_factor": 1.0, | |
| "expert_expansion": 1.5, | |
| "experts_per_token": 2, | |
| "load_balancing_weight": 0.01, | |
| "n_experts": 4 | |
| }, | |
| "n_heads": 9, | |
| "n_layers": 12, | |
| "pad_token_id": 32759, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.14.1", | |
| "use_cache": false, | |
| "vocab_size": 32768 | |
| } | |