* common : increase max number of experts to 160
* common : add tensors ATTN_Q_A, ATTN_Q_A_NORM, ATTN_Q_B, ATTN_KV_A_MQA, ATTN_KV_A_NORM, ATTN_KV_B needed by DeepSeek-V2 MLA (multi-head latent attention) architecture
* common : add model header parameters: leading_dense_block_count, expert_feed_forward_length, expert_shared_count, expert_weights_scale, attention.q_lora_rank, attention.kv_lora_rank, rope.scaling.yarn_log_multiplier
* convert-hf : add model conversion support for DeepseekV2ForCausalLM
* llama : add model types for DeepSeek-V2 and DeepSeek-V2-Lite models
* llama : add two new llm_build_moe_ffn() arguments: scale_w (whether to scale weights of selected MoE experts) and w_scale (numerical value of the scaling factor)
* llama : add inference support for LLM_ARCH_DEEPSEEK2
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* common : increase max number of experts to 128
* common : add tensor LLM_TENSOR_FFN_NORM_EXPS for normalization before MoE that runs in parallel to attention + ffn
* gguf-py : add architecture-specific block mappings that override selected general block mappings
* convert-hf : add model conversion support for ArcticForCausalLM
* convert-hf : use added_tokens_decoder from tokenizer_config.json to redefine tokens from SentencePiece model (only for ArcticForCausalLM)
* llama : add inference support for LLM_ARCH_ARCTIC
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* convert-hf : add conversion of bloom-style qkv tensor to gpt-style qkv (code borrowed from BloomModel)
* llama : add inference support for LLM_ARCH_GPTNEOX
* llama : add model types for every Pythia variant and GPT-NeoX
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* llama : use n_embd_head_v instead of n_embd_head_k when reshaping kqv
* llama : use n_embd_v_gqa and n_embd_head_v instead of n_embd_k_gqa and n_embd_head_k when making a view of cached value vectors.
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Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
