rh'XdZddlmZmZddlmZddlmZmZm Z m Z ddl m Z ejeZGddeZGd d e Zed Gd deZed Gdde Zed GddeZed Gdde ZgdZy)zPyTorch Arcee model.)auto_docstringlogging) LlamaConfig)LlamaForCausalLMLlamaForQuestionAnsweringLlamaForSequenceClassificationLlamaForTokenClassification) NemotronMLPcdeZdZdZdZdddddddZ dfd ZxZS) ArceeConfiga This is the configuration class to store the configuration of a [`ArceeModel`]. It is used to instantiate an Arcee 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 AFM-4.5B-Base. Pre-trained weights are available at [arcee-ai/AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) and were used to build the examples below. 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 32000): Vocabulary size of the Arcee model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ArceeModel`] hidden_size (`int`, *optional*, defaults to 2560): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 18432): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. 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*): 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 checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 4096): The maximum sequence length that this model might ever be used with. AFM-4.5B-Base supports up to 16384 tokens. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. 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`. pad_token_id (`int`, *optional*): Padding token id. bos_token_id (`int`, *optional*, defaults to 128000): Beginning of stream token id. eos_token_id (`int`, *optional*, defaults to 128001): End of stream token id. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings rope_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'yarn'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'yarn'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. attention_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. mlp_bias (`bool`, *optional*, defaults to `False`): Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers. head_dim (`int`, *optional*): The attention head dimension. If None, it will default to hidden_size // num_attention_heads ```python >>> from transformers import ArceeModel, ArceeConfig >>> # Initializing an Arcee AFM-4.5B-Base style configuration >>> configuration = ArceeConfig() >>> # Initializing a model from the AFM-4.5B-Base style configuration >>> model = ArceeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```arceecolwiserowwise)zlayers.*.self_attn.q_projzlayers.*.self_attn.k_projzlayers.*.self_attn.v_projzlayers.*.self_attn.o_projzlayers.*.mlp.up_projzlayers.*.mlp.down_projc t|did|d|d|d|d|d|d|d|d | d | d | d | d | d|d|d|d|d|d|d|d|||`y)N vocab_size hidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsnum_key_value_heads hidden_actmax_position_embeddingsinitializer_range rms_norm_eps use_cache pad_token_id bos_token_id eos_token_idtie_word_embeddings rope_theta rope_scalingattention_biasattention_dropoutmlp_biashead_dim)super__init__pretraining_tp)selfrrrrrrrrrrrrrrr r!r"r#r$r%r&kwargs __class__s z/var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/arcee/modular_arcee.pyr)zArceeConfig.__init__s2  ! # 0 0  !4  !4  " %< 0 &   & & & !4 "! "&# $*% &0' () *- 2  )i}i iH r0Nrelu2ig{Gz?gh㈵>TNiiFg@NFgFN)__name__ __module__ __qualname____doc__ model_typebase_model_tp_planr) __classcell__)r-s@r.r r sv_ BJ%.%.%.%. )"+   $!-2 2 r/r c eZdZy)ArceeMLPNr2r3r4r'r/r.r:r:sr/r:zarcee-ai/AFM-4.5B) checkpointc eZdZy)ArceeForCausalLMNr;r'r/r.r>r>r/r>c eZdZy)ArceeForSequenceClassificationNr;r'r/r.rArAr?r/rAc eZdZy)ArceeForQuestionAnsweringNr;r'r/r.rCrCr?r/rCc eZdZy)ArceeForTokenClassificationNr;r'r/r.rErEr?r/rE)r r>rCrArE ArceeModelArceePreTrainedModelN)r5transformers.utilsrrllama.configuration_llamarllama.modeling_llamarrr r nemotron.modeling_nemotronr get_loggerr2loggerr r:r>rArCrE__all__r'r/r.rOs63 5   H %^ +^ B { ./ ' 0 ./ %C 0 ./ 9 0 ./ "= 0  r/