* lora: load to devide buft
* add patch tensor function
* correct tensor patch
* llama_lora_adapter_apply
* correct ggml_backend_tensor_copy
* add llm_build_mm
* fix auto merge
* update based on review comments
* add convert script
* no more transpose A
* add f16 convert
* add metadata check
* add sanity check
* fix ftype
* add requirements
* fix requirements
* fix outfile
* conversion: only allow selected models
* fix types
* cuda : do not use dmmv if the tensor does not have enough cols
* llama : lora fixes
* do not disable mmap with lora
Co-authored-by: slaren <slarengh@gmail.com>
* llm_build_lora_mm_id
* convert_lora : MoE LoRA conversion support
* convert_lora : prefer safetensors, similarly to convert_hf
* convert_hf : simplify modify_tensors for InternLM2
* convert_lora : lazy conversion
* llama : load and use alpha from LoRA adapters
* llama : use llm_build_lora_mm in most model graphs
* auto scale
* Revert "auto scale"
This reverts commit 42415a4874.
* remove redundant params
* Apply suggestions from code review
Co-authored-by: slaren <slarengh@gmail.com>
* change kv metadata
* move add_type to __init__
* convert_hf : move add_type to main()
* convert_lora : use the GGUFWriter from Model instead of overwriting it
---------
Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Francis Couture-Harpin <git@compilade.net>
This commit adds a macro guard to pragma GCC to avoid the following
warning on windows:
```console
C:\llama.cpp\ggml\src\ggml-aarch64.c(17,9): warning C4068:
unknown pragma 'GCC' [C:\lama.cpp\build\ggml\src\ggml.vcxproj]
```
The README.md had a stale information. In particular, the --ctx-size
"defaults to 512" confused me and I had to check the code to confirm
this was false. This the server is evolving rapidly, it's probably
better to keep the source of truth at a single place (in the source) and
generate the README.md based on that.
Did:
make llama-server
./llama-server --help > t.txt
vimdiff t.txt examples/server/README.md
I copied the content inside a backquote block. I would have preferred
proper text but it would require a fair amount of surgery to make the
current output compatible with markdown. A follow up could be to
automate this process with a script.
No functional change.
* 9B - query_pre_attn_scalar = 256 not 224
See 03e657582d
Gemma 9b should use 256 and not 224 (self.config.hidden_size // self.config.num_attention_heads)
* llama : fix Gemma-2 Query scaling factor
ggml-ci
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* llama : fix mpt and olmo pre-tokenizer
* llama : pre-tokenize non-special user-defined tokens first
* llama : fix detection of control-like user-defined tokens
* convert_hf : identify which user-defined tokens are control tokens
Only used in _set_vocab_gpt2() for now.
* convert_hf : identify more added control tokens for SPM tokenziers
This makes Gemma and Gemma-2 tokenize pretty much EVERYTHING correctly,
including HTML tags and consecutive spaces,
but it unfortunately requires model re-conversion.
There seems to be a weird behavior of the HF tokenizer for Gemma,
which prefers to use the 16-space token over more lengthy space tokens,
while using the SentencePiece tokenizer does not do this.
(the implementation in llama.cpp has the same behavior as SentencePiece)
* llama : fix wrong pre-tokenization of byte tokens
* llama : fix Viking pre-tokenizer regex
The order was previously wrong, which caused errors in some tests.
* llama : fix command-r detokenization
* convert_hf : reduce usages of the UNKNOWN token type
* llama : add UNKNOWN tokens in the special tokens cache
* convert_hf : reduce usages of UNKNOWN for InternLM2
This makes the changes from #8321 more consistent
with the other changes made here.
* test-tokenizer-random : reduce potential confilcts with #8379
* test-tokenizer-random : add a failing edge case for falcon
