Instructions to use iamEvanYT/K2-Horizon-MoVA-36B-A4B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use iamEvanYT/K2-Horizon-MoVA-36B-A4B-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir K2-Horizon-MoVA-36B-A4B-MLX-4bit iamEvanYT/K2-Horizon-MoVA-36B-A4B-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,624 Bytes
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"architectures": [
"K2HorizonForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attention_gate_func": "softplus",
"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": 6144,
"layernorm_num_groups": 2,
"max_position_embeddings": 524288,
"mlp_only_layers": [
0,
1,
2
],
"model_type": "k2_horizon",
"moe_gate_bias": true,
"moe_intermediate_size": 768,
"mova_num_experts": 64,
"mova_num_experts_per_tok": 4,
"norm_topk_prob": true,
"num_attention_heads": 32,
"num_experts": 100,
"num_experts_per_tok": 8,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"num_shared_experts": 1,
"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": 2.5,
"router_score_func": "sigmoid",
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.13.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 250624,
"model_file": "k2_horizon.py",
"quantization": {
"group_size": 64,
"bits": 4,
"mode": "affine"
}
}
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