Text Generation
Transformers
Safetensors
lfm2_moe_custom
liquid
lfm2.5
edge
conversational
custom_code
Eval Results
Instructions to use deepnevro/or with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepnevro/or with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepnevro/or", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("deepnevro/or", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepnevro/or with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepnevro/or" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepnevro/or", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepnevro/or
- SGLang
How to use deepnevro/or 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 "deepnevro/or" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepnevro/or", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "deepnevro/or" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepnevro/or", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepnevro/or with Docker Model Runner:
docker model run hf.co/deepnevro/or
| { | |
| "architectures": [ | |
| "Lfm2MoeCustomForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_lfm2_moe_custom.Lfm2MoeCustomConfig", | |
| "AutoModelForCausalLM": "modeling_lfm2_moe_custom.Lfm2MoeCustomForCausalLM" | |
| }, | |
| "bos_token_id": 124894, | |
| "budget": 1, | |
| "conv_L_cache": 3, | |
| "conv_bias": false, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 124900, | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 7168, | |
| "layer_types": [ | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv" | |
| ], | |
| "max_position_embeddings": 128000, | |
| "model_type": "lfm2_moe_custom", | |
| "moe_intermediate_size": 1792, | |
| "norm_eps": 1e-05, | |
| "norm_topk_prob": true, | |
| "num_attention_heads": 32, | |
| "num_dense_layers": 2, | |
| "num_experts": 32, | |
| "num_experts_per_tok": 4, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 124893, | |
| "rope_parameters": { | |
| "rope_theta": 5000000, | |
| "rope_type": "default" | |
| }, | |
| "routed_scaling_factor": 1.0, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.9.0", | |
| "use_cache": true, | |
| "use_expert_bias": true, | |
| "vocab_size": 128000 | |
| } |