Instructions to use EvoNet/EvoNet-AI-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EvoNet/EvoNet-AI-V1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EvoNet/EvoNet-AI-V1")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("EvoNet/EvoNet-AI-V1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EvoNet/EvoNet-AI-V1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EvoNet/EvoNet-AI-V1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EvoNet/EvoNet-AI-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EvoNet/EvoNet-AI-V1
- SGLang
How to use EvoNet/EvoNet-AI-V1 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 "EvoNet/EvoNet-AI-V1" \ --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": "EvoNet/EvoNet-AI-V1", "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 "EvoNet/EvoNet-AI-V1" \ --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": "EvoNet/EvoNet-AI-V1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use EvoNet/EvoNet-AI-V1 with Docker Model Runner:
docker model run hf.co/EvoNet/EvoNet-AI-V1
| { | |
| "architectures": [ | |
| "MamLAForCausalLM" | |
| ], | |
| "bos_token_id": 1, | |
| "d_model": 2048, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "mamba_d_conv": 4, | |
| "mamba_d_state": 64, | |
| "mamba_expand": 2, | |
| "mamba_layers_per_cycle": 7, | |
| "mamba_n_heads": 16, | |
| "mla_kv_lora_rank": 128, | |
| "mla_layers_per_cycle": 1, | |
| "mla_n_heads": 16, | |
| "mla_q_lora_rank": 128, | |
| "mla_qk_nope_head_dim": 32, | |
| "mla_qk_rope_head_dim": 32, | |
| "mla_v_head_dim": 32, | |
| "model_type": "mamla", | |
| "moe_d_ff": 1024, | |
| "moe_num_experts": 4, | |
| "moe_top_k": 2, | |
| "n_layer": 24, | |
| "num_experts": 4, | |
| "num_experts_per_tok": 2, | |
| "num_layers": 16, | |
| "pad_token_id": 0, | |
| "pad_vocab_size_multiple": 8, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "vocab_size": 32000 | |
| } | |