* convert ok
* no deepstack
* less new tensors
* cgraph ok
* add mrope for text model
* faster patch merger
* add GGML_ROPE_TYPE_MRNORM
* add support for metal
* move glm4v do dedicated graph
* convert: add norm_embd
* clip: add debugging fn
* working correctly
* fix style
* use bicubic
* fix mrope metal
* improve cpu
* convert to neox ordering on conversion
* revert backend changes
* force stop if using old weight
* support moe variant
* fix conversion
* fix convert (2)
* Update tools/mtmd/clip-graph.h
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* process mrope_section on TextModel base class
* resolve conflict merge
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add support for NVIDIA Nemotron Nano 3
This commit adds support for the NVIDIA Nemotron Nano 3 model, enabling
the conversion and running of this model.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* [model] add glm-asr support
* fix format for ci
* fix convert format for ci
* update glm_asr convert script & use build_ffn for glm_asr clip & use build_stack for padding and review
* check root architecture for convert hf script
* fix conficlt with upstream
* fix convert script for glm asr & format clip-impl
* format
* restore hparams text
* improved conversion
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Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* conversion: use existing local chat_template.jinja file if mistral-format model has one.
* fix --mistral-format mistakenly assuming some <=v7 chat template names are file paths and reading them.
* Update convert_hf_to_gguf.py - change from exists() to is_file()
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
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Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Qwen3 Next - cleaned up version
* Whitespaces and stuff
* Correct minor errors
* Update src/llama-model.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Misc. fixes.
* Clean up code, add missing hybrid qualifier
* Did someone transpose the SOLVE_TRI result matrix? Perhaps...
* Whitespace
* Proper tensors for cb calls
* Use llama-graph.h vertical alignment
* BROKEN: chunking
* Set new tensors as inputs.
* Proper chunk logic
* It's the circle of life...
* More shenanigans for n_seq > 1
* Nail in the coffin?
* Fix Windows build
* Eh, one fails on Windows, the other fails on Mac... just use general capture.
* quant : cleanup
* model : cleanup
* qwen3 : cleanup
* cont : cleanup
* cont : cleanup
* ggml : revert change
* qwen3 : cleanup
* cont : cleanup
* Readd cmath
* qwen3 : fix typo
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Usual suspects
* fix my bad suggestion
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Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Fix convert_hf_to_gguf.py script on s390x
Assume converted model data is originally little-endian.
Byteswap data on s390x after reading it to put values in correct presentation
for any transformation needed, like calculating weight tensors.
Then byteswap data to little-endian before passing it to GGUFWriter while
GGUFWriter will byteswap data back to big endian if big endian output is requested.
byteswap(inplace=True) calls don't work with lazy tensor and array wrappers.
Use byteswap with copying data to workaround this behaviour.
* Make GGUFWriter accept tensors in native endianness instead of little-endian
With this change if no byteswapping is actually needed, 2 excessive byteswaps can be omitted on s390x
* Fix byteswapping in convert_hf_to_gguf.py for remote models
Register UMT5Model as a supported architecture variant for T5 model conversion.
This allows the conversion to work for models downloaded with AutoModel.
* convert : parse safetensors directly
* gguf-py : order safetensors tensors by name
Applies to both local and remote safetensors custom parsing.
This matches the behavior of the official safetensors implementation.
* convert : rename from_safetensors_meta to from_local_tensor
For consistency with from_remote_tensor
* convert : fix no-lazy dtypes from direct safetensors
Saba Fallah
Xuan-Son Nguyen
Daniel Bevenius
Sigbjørn Skjæret
HelloKS
piDack
Georgi Gerganov
philip-essential
bluebread
SmartestWashingMachine
Piotr Wilkin (ilintar)
Aleksei Nikiforov
william pan
Bartowski
levkropp
compilade
Li Pengzhan