Text Generation
Transformers
Safetensors
English
German
multilingual
k2_horizon
ocean-horizon
3.7b
dense
open-weights
oceanlabs
long-context
reasoning
agentic
custom_code
Instructions to use OceanLabs/Ocean-3.7B-coding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OceanLabs/Ocean-3.7B-coding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OceanLabs/Ocean-3.7B-coding", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("OceanLabs/Ocean-3.7B-coding", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OceanLabs/Ocean-3.7B-coding with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OceanLabs/Ocean-3.7B-coding" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OceanLabs/Ocean-3.7B-coding", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OceanLabs/Ocean-3.7B-coding
- SGLang
How to use OceanLabs/Ocean-3.7B-coding 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 "OceanLabs/Ocean-3.7B-coding" \ --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": "OceanLabs/Ocean-3.7B-coding", "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 "OceanLabs/Ocean-3.7B-coding" \ --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": "OceanLabs/Ocean-3.7B-coding", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OceanLabs/Ocean-3.7B-coding with Docker Model Runner:
docker model run hf.co/OceanLabs/Ocean-3.7B-coding
File size: 1,807 Bytes
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"architectures": [
"K2HorizonForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attention_gate_func": null,
"auto_map": {
"AutoConfig": "configuration_k2_horizon.K2HorizonConfig",
"AutoModel": "modeling_k2_horizon.K2HorizonModel",
"AutoModelForCausalLM": "modeling_k2_horizon.K2HorizonForCausalLM"
},
"bos_token_id": 0,
"decoder_sparse_step": 1,
"dtype": "bfloat16",
"eos_token_id": 1,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 10240,
"layernorm_num_groups": 2,
"max_position_embeddings": 524288,
"mlp_only_layers": [
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],
"model_type": "k2_horizon",
"moe_gate_bias": false,
"moe_intermediate_size": 0,
"mova_num_experts": 0,
"mova_num_experts_per_tok": 0,
"norm_topk_prob": true,
"num_attention_heads": 32,
"num_experts": 0,
"num_experts_per_tok": 0,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"num_shared_experts": 0,
"output_router_logits": false,
"pad_token_id": null,
"query_key_norm": false,
"rms_norm_eps": 1e-06,
"rope_head_dim": 128,
"rope_parameters": {
"rope_theta": 10000000.0,
"rope_type": "default"
},
"router_aux_loss_coef": 0.001,
"router_scaling_factor": 1.0,
"router_score_func": "sigmoid",
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.17.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 250624,
"_name_or_path": "OceanLabs/Ocean-Horizon-3.7B"
}
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