.`iZ@ddlmZddlmZGddeZdgZdS))Any)PretrainedConfigc2eZdZdZdZdgZ d.dedededededededededee e fd zded ed!e d"ed#ed$ed%ed&ed'ed(ed)ed*e d+ed,e e d zf0fd- Z xZS)/ Lfm2MoeConfiga This is the configuration class to store the configuration of a [`Lfm2MoeModel`]. It is used to instantiate a LFM2 Moe model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the LFM2-8B-A1B model. e.g. [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B) Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 65536): Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Lfm2Model`] hidden_size (`int`, *optional*, defaults to 2048): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 7168): Dimension of the MLP representations. moe_intermediate_size (`int`, *optional*, defaults to 1792): Intermediate size of the routed expert. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. pad_token_id (`int`, *optional*, defaults to 0): Padding token id. bos_token_id (`int`, *optional*, defaults to 1): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 2): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings rope_parameters (`dict`, *optional*): The parameters of the RoPE embeddings. max_position_embeddings (`int`, *optional*, defaults to 128000): The maximum sequence length that this model might ever be used with. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (`int`, *optional*, defaults to 8): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `num_attention_heads`. conv_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the conv layers. conv_L_cache (`int`, *optional*, defaults to 3): L_cache dim in the conv layers. num_dense_layers (`int`, *optional*, defaults to 2): Number of dense Lfm2MoeMLP layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head). num_experts_per_tok (`int`, *optional*, defaults to 4): Number of selected experts. num_experts (`int`, *optional*, defaults to 32): Number of routed experts. use_expert_bias (`bool`, *optional*, defaults to `True`): Whether to use the expert bias on the routing weights. routed_scaling_factor (`float`, *optional*, defaults to 1.0): Scaling factor for routed experts in MoE models. norm_topk_prob (`bool`, *optional*, defaults to `True`): Whether to normalize the topk probabilities. layer_types (`Optional`, *optional*): Type of each layers. ```python >>> from transformers import Lfm2MoeModel, Lfm2MoeConfig >>> # Initializing a LFM2 Moe model >>> configuration = Lfm2MoeConfig() >>> # Initializing a model from the LFM2-8B-A1B style configuration >>> model = Lfm2MoeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```lfm2_moepast_key_values rTNh㈵>F? vocab_size hidden_sizeintermediate_sizemoe_intermediate_sizenum_hidden_layers pad_token_id bos_token_id eos_token_idtie_word_embeddingsrope_parametersmax_position_embeddings use_cachenorm_epsnum_attention_headsnum_key_value_heads conv_bias conv_L_cachenum_dense_layersnum_experts_per_tok num_expertsuse_expert_biasrouted_scaling_factornorm_topk_prob layer_typesc ||_||_||_||_|dd}| d|d} | |_| |_| |_| |_||_ ||_ ||_ ||_ ||_ ||_||_||_||_||_||_||_|d| } t-jd|||| d|dS)N rope_thetag.Adefault) rope_typer/ tie_embedding)rrrr)rrrrpoprr r!r"r#r$r%r&r'rr(r)r*r+r,r-getsuper__init__)selfrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-kwargsr/ __class__s |/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/vllm/transformers_utils/configs/lfm2_moe.pyr7zLfm2MoeConfig.__init__]s.8%&!2!2ZZ i88  ",5ZPPO.'>$"  $7 #6 #(!1%:"#6 &.%:",&$jj 0    %%% 3         )r r r r r rrrTNrTrr rFrrrr TrTN)__name__ __module__ __qualname____doc__ model_typekeys_to_ignore_at_inferenceintbooldictstrrfloatlistr7 __classcell__)r:s@r;rrsO O bJ#4"5 !%%)!#$(15'.!#%#$ !#$ $'*#(,3C C C C  C # C  C C C C "C c3h$.C "%C C C !C !!C "#C $%C &'C (!)C *+C ,-C . %/C 01C 2#Y%3C C C C C C C C C C r<rN)typingr transformers.configuration_utilsrr__all__r3r<r;rMsm======X X X X X $X X X v  r<