* Update random test: add_bos_token.
* Update random test: add WPM models for testing.
* Build vocab.special_tokens_cache using vocab token types.
* Fix and improve WPM preprocessing.
- Fix unicode edge case combinations.
- Split by whitspace in the same pass.
* Discard all tokens when no matching found.
* Add optional MLP bias for Granite models
Add optional MLP bias for ARCH_LLAMA to support Granite models.
Partially addresses ggerganov/llama.cpp/issues/7116
Still needs some more changes to properly support Granite.
* llama: honor add_space_prefix from the model configuration
propagate the add_space_prefix configuration from the HF model
configuration to the gguf file and honor it with the gpt2 tokenizer.
Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>
* llama: add support for small granite models
it works only for the small models 3b and 8b.
The convert-hf-to-gguf.py script uses the vocabulary size of the
granite models to detect granite and set the correct configuration.
Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>
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Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>
Co-authored-by: Steffen Roecker <sroecker@redhat.com>
* 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>
