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
File size: 1,400 Bytes
7945f84 44730d5 7945f84 be73072 7945f84 | 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 65 66 | {
"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
} |