* llama : add support for Nemotron 3 Super
This commit adds support for the Nemotron 3 Super model (120B.A12B)
enabling this model to be converted to GGUF format and run in llama.cpp.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Matt Clayton <156335168+mattjcly@users.noreply.github.com>
* llama-quant : correct `n_attention_wv` usage
In #19770, I introduced a regression in the way the
`quantize_state_impl` counter values were initialized. I was
incrementing and using `n_attention_wv` in the same loop, when it should
have been fixed by the time we're deciding tensor types in
`llama_tensor_get_type_impl` (for `use_more_bits`).
I never observed a difference in any of [my
tests](https://github.com/ggml-org/llama.cpp/pull/19770#issuecomment-4000424712)
- it was only after @bartowski kindly pointed this out that I realized
it was incorrect. (Thanks!)
* simplify
* K quant speedup (#20)
* Basic JIT compilation for mul_mat, get_rows, and scale (#17)
* scale jit working
* preliminary working jit for getrows and mulmat, needs refining
* simplified mul_mat preprocessing switch statement
* get_rows fixes, mul_mat refinement
* formatted + last edits
* removed some extraneous prints
* fixed get_rows, fixed workgroup dispatch in mul_mat. no gibberish
* small fix
* some changes, working
* get_rows and mul_mat jit fixed and working
* Update formatting
* formatting
* Add header
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Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Start work on all-encompassing shader library
* refactor argmax, set_rows
* Refactor all but flashattention, mat mul
* no gibberish, all k quants added, merged
* vec memory fix
* q6_k matching metal on my machine, tests passing
* Set tile size for q6_k separately
* Separate out fast shaders
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Co-authored-by: neha-ha <137219201+neha-ha@users.noreply.github.com>
* Move towards writeBuffer for params
* Move away from multiple buffers for set_rows errors, remove host buffer for parameter buffers, minor cleanups
* Remove extra file
* Formatting
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Co-authored-by: neha-ha <137219201+neha-ha@users.noreply.github.com>
* Fix logic for retrieving schema items in `json_schema_to_grammar.py`
If `schema['items']` is `{}` and `prefixItems not in schema', as `{}` is Falsy, the original code here will raise an error.
I think if `schema['items']` is `{}`, them items should just be `{}`
* Apply suggestion from @CISC
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Add tests for arrays with empty items
Add two unit tests to `tests/test-json-schema-to-grammar.cpp` that validate handling of arrays when 'items' is an empty schema and when 'prefixItems' is present alongside an empty 'items'. Both tests expect the same generated grammar, ensuring the JSON Schema->grammar conversion treats an empty 'items' schema (and the presence of 'prefixItems') correctly and covering this edge case.
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Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fix corner case in sd_oai_transform_params
Also fix typo in the function name.
* support for customizing loaded LoRA multipliers
The `sdloramult` flag now accepts a list of multipliers, one for each
LoRA. If all multipliers are non-zero, LoRAs load as before, with no extra
VRAM usage or performance impact.
If any LoRA has a multiplier of 0, we switch to `at_runtime` mode, and these
LoRAs will be available to multiplier changes via the `lora` sdapi field and
show up in the `sdapi/v1/loras` endpoint. All LoRAs are still preloaded on
startup, and cached to avoid file reloads.
If the list of multipliers is shorter than the list of LoRAs, the multiplier
list is extended with the first multiplier (1.0 by default), to keep it
compatible with the previous behavior.
* support for `<lora:name:multiplier>` prompt syntax and metadata
* add a few tests for sanitize_lora_multipliers
Replace GGML_ABORT("fatal error") in ggml_metal_synchronize() with
error flag + return. This aligns synchronize error handling with
graph_compute, which already returns GGML_STATUS_FAILED for the same
condition.
When a command buffer fails (e.g., iOS GPU access revocation during
backgrounding, macOS eGPU disconnect, OOM), the backend enters an
error state instead of killing the host process. Subsequent
graph_compute calls return GGML_STATUS_FAILED immediately. Recovery
requires recreating the backend.
Failed extra command buffers are properly released on the error path
to avoid Metal object leaks.
* quantize : imatrix-fail early + code cleanup
* fix manual override printing
it's in the preliminary loop now, so needs to be on its own line
* revert header changes per ggerganov
* remove old #includes
* clarify naming
rename `tensor_quantization` to `tensor_typo_option` to descirbe its
functionality
* fix per barto
* Parse port numbers from MCP server URLs
* Pass scheme to http proxy for determining whether to use SSL
* Fix download on non-standard port and re-add port to logging
* add test
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Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Enable mul_mv_ext small-batch kernels (BS 2-8) for BF16, Q2_K,
and Q3_K quantization types. These types previously fell through
to the slower single-row mul_mv path.
BF16 uses the float4 dequantize path (like F16). Q2_K and Q3_K
use the float4x4 K-quant path (like Q4_K/Q5_K/Q6_K).
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Georgi Gerganov
Daniel Bevenius
Concedo
Aman Gupta
Neo Zhang
Ruben Ortlam
Aldehir Rojas
Piotr Wilkin (ilintar)
uvos
Pascal
Alessandro de Oliveira Faria (A.K.A.CABELO)
Vinicios Lugli
ddh0
Reese Levine
Ray Xu
a3894281
Wagner Bruna
Sigbjørn Skjæret
Charles Xu
Taimur Ahmad
Julian Pscheid
ddh0
Xuan-Son Nguyen
Evan Huus
Paul Flynn