You’re building a character. And how you’re doing that is you’re doing it with detail. A novel is a thousand details. It may be two thousand. And that the best way of describing this is to look at how Cezanne built up a painting. In tiny little daubs, each daub having a tiny...
if self.q_lora_rank is None: self.q_proj = nn.Linear( self.hidden_size, self.num_heads * self.q_head_dim, bias=False ) else: self.q_a_proj = nn.Linear( self.hidden_size, config.q_lora_rank, bias=config.attention_bias ) self.q_a_layernorm = DeepseekV3RMSNorm(co...
just like what intelligence should mean is not obvious, so we need to find some way of defining that. It’s also ambiguous what it means for an LLM to produce writing in the first place. Here I’m going to assume that an LLM producing writing involves a human providing the prompt and ...
Acute promyelocytic leukemia (APL) is driven by the specific fusion gene PML-RARA produced by chromosomal translocation. Three classic isoforms, L, V, and
f"past key much have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got" f" {past_key.shape}" ) past_key_value = (past_key, past_value) if attention_mask is not None: attention_mask = attention_mask[:, slicing_tokens:] ...
A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF) - trlx/trlx/models/modeling_ppo.py at main · CarperAI/trlx
Other data from this experiment are also well described by both model results obtained by averaging the corresponding multi-parametric matrices over an \\(Y(A,\\mathrm{TKE})\\) distribution measured at JRC-Geel. This is considered as a secondary validation (i.e., of the models together ...
Spatial visualization of communities’ resilience can be used to inspect disaster equity-related quantities of interest. Uncertainty propagation is considered across the different models in (a–d). For the sake of simplicity, probabilistic nature is only depicted at the level of functionality curves ...
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`] Args: config: PhiConfig """ def __init__(self, config: PhiConfig): super().__init__(config) self.padding_idx = config.pad_token_id ...
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`IndexDecoderLayer`] Args: config: IndexConfig """ def __init__(self, config: IndexConfig): super().__init__(config) self.padding_idx = config.pad_token_id self.vocab_size = config.vocab...