Feature Extraction
Transformers
Safetensors
English
arcee_kda
kda
kimi-delta-attention
linear-attention
distillation
research
custom_code
Instructions to use CollectionStudio/AFM-4.5B-Base-KDA-Only with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/AFM-4.5B-Base-KDA-Only with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CollectionStudio/AFM-4.5B-Base-KDA-Only", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CollectionStudio/AFM-4.5B-Base-KDA-Only", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,388 Bytes
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"architectures": [
"ArceeKDAForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_arcee_kda.ArceeKDAConfig",
"AutoModel": "modeling_arcee_kda.ArceeKDAForCausalLM",
"AutoModelForCausalLM": "modeling_arcee_kda.ArceeKDAForCausalLM"
},
"bos_token_id": 128000,
"dtype": "bfloat16",
"eos_token_id": 128001,
"head_dim": 128,
"hidden_act": "relu2",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 18432,
"linear_attn_config": {
"full_attn_layers": [],
"head_dim": 128,
"kda_layers": [
1,
2,
3,
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],
"num_heads": 20,
"short_conv_kernel_size": 4
},
"max_position_embeddings": 32768,
"mlp_bias": false,
"model_type": "arcee_kda",
"num_attention_heads": 20,
"num_hidden_layers": 36,
"num_key_value_heads": 4,
"pad_token_id": 128001,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": false,
"vocab_size": 128032
}
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