* model : add Kolibri-1 (Aleph-Alpha) support
Kolibri-1 is a bilingual German/English MoE reasoning model with 78B
parameters and 3.46B active per token: 50 layers, 384 routed experts plus
one shared expert.
Attention interleaves four sliding layers at a 513 window with one full
layer. The sliding layers rotate with NeoX RoPE, the full layers are NoPE.
GQA runs 48 query heads against 4 KV heads at head_dim 128 with a
QK-RMSNorm, and every block carries four RMSNorms, so the attention and the
MLP output are normalized before they rejoin the residual stream. Routing
picks the experts from logits + expert_bias and weights them with the
sigmoid of the unbiased logits, without renorm.
The converter reuses AfmoeModel for the per-expert checkpoint layout and
the expert selection bias, and writes the shared expert and the per layer
sliding window pattern. The released FP8 checkpoint needs no special code,
it goes through the generic weight_scale_inv path. Three tensor names are
added to the mapping (post_attn_norm, post_ffn_norm,
moe.router.expert_bias) and the 128k vocabulary registers the qwen2
pre-tokenizer by hash. The arch is registered in test-llama-archs, with GQA
and the sliding window pattern.
Assisted-by: DeepSeek Harness (DeepSeek V4.1 Flash)
* Update src/models/kolibri1.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Add fusion records for kolibri1
* Update src/models/kolibri1.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
The scheduler uploads each view of a host input on its own, so the per-layer reshape of new_pool_idxs (and of sel_mask in glm5-next) was copied once per layer.
* model: add Muse Spark Open (text, vision and audio)
Muse Spark Open is a latent space MoE: the residual stream stays at 8192
while the 256 experts live in 4096, with a projection down and up around
them, top-8 sigmoid routing with a correction bias. Attention interleaves
three sliding layers at a 2048 window with rope for one global layer
without rope, with a sigmoid output gate and a weightless QK-norm.
Residuals are norm preserving, their gate is folded at conversion time.
Vision reuses the Muse Glimmer tower. Audio is a causal convolutional
pyramid with no attention and no spectrogram, four stages folding 16, 6,
5 and 4 samples into the channel dim, one embedding per 80 ms of 24 kHz.
The quantizer keeps the latent projections and the attention gate at
Q8_0, a little over 1% of the weights, since the whole residual stream
passes through them and the low-bit types turned the output into a
repeating loop. The model saver writes the post norm epsilon, and the
arch is registered in test-llama-archs.
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
* Muse Spark: drop the redundant forward expand
* Muse Spark: drop the redundant forward expand and tighten the comments
* Update src/llama-quant.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Update conversion/muse_spark.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Add Support to maion-coder arch
* iAdd Support to support Draft models
* Address comments
* Remove maion context shift function
---------
Co-authored-by: Karthik Vijayan <kavijay@microsoft.com>
* llama : split llama_prefetch() into merge and prefetch steps
Merge the ranges first, then call the system functions.
This fixes -Wunreachable-code-return on Windows.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* Use const_cast
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---------
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* llama: honor PARTIAL_ONLY in the plain KV cache
A non-SWA llama_kv_cache rolls back with seq_rm, so it holds no
partial state, yet state_write ignored the flag and serialized every
cell. A draft context made of a plain KV cache (MTP head, dense -md
draft) therefore copied its whole KV into each server checkpoint,
growing it by the draft KV size per token.
The cache now writes and reads nothing for PARTIAL_ONLY unless it is
SWA, and the iSWA wrapper no longer gates its base cache since it
filters itself.
* Update src/llama-kv-cache.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama: delegate PARTIAL_ONLY to the caches of every memory wrapper
The hybrid, hybrid-idx and dsa-iswa wrappers skipped their attention
caches on PARTIAL_ONLY, which also dropped an SWA window that is
partial state. Each cache now applies the flag itself, so the
wrappers only delegate. The indexer is marked stale only when a
restore rewrites cells.
test-llama-context gains a case checking that the partial state is
not larger than the full state and, without SWA, does not grow with
the tokens.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
An embedding-only graph has no logits, and build_pooling() returns
early when embeddings are off, so since the models no longer expand
t_embd the graph ends up empty and its inputs are never allocated.
Expand t_embd in that case.
* spec : add lilicorr support
* convert : use convention for hparam assignment
* add todo for hparam use in llama-batch.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* typo fix
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
When every device reports zero free memory the split points are
normalized by a zero sum, the NaN split sends layers to a device index
past the end and loading aborts with vector::_M_range_check.
These graphs only produce embeddings, and build_pooling() already
expands its output for every pooling type, so the expand at the end of
the model graph is redundant.
* llama : build the k-pool selection in the causal mask layout
- the glm5-next indexer folded the causal mask in through a [n_kv, n_tokens]
view: one reshape of the mask ahead of the add, and one of the result ahead
of every layer reading the selection, including the shared-indexer layers
- build the selection directly in the mask layout, as qwen4exp already does,
and drop the now-degenerate reshape in its attention as well
- assert the selection width matches the input it is taken from
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* cont : remove comment
* unicode : add opt-in NFC normalization for Unigram tokenizers
Note: used help from Codex, Sol 6.1 Medium
* Fixing initial bot comments + linter error (using existing out instead of print)
* removed test_nfc_tokenizer
The no-cache attention pads K, V and the mask to 256, but T5 also adds
a relative position bias of [n_tokens, n_tokens], so the add with KQ
fails unless n_tokens divides the padded width. Pad the bias with
zeros up to the KV dimension of the mask, like K and V.
* llama : do not map foreign ranges into a backend buffer
A buffer created from a host pointer pins everything that it wraps. When
several contexts share the same file (lazy tensors, MoE tensors kept on the
CPU with -cmoe), the single buffer that spans the first and the last tensor
of a context also wraps the ranges owned by the other contexts, pinning data
that the backend never reads.
For models with per-layer token embeddings this pins the whole lazy tensor
into the GPU buffer (27 GiB for Qwen3.8-Flash-Next IQ4_NL), defeating the
point of reading it lazily.
Instead, compute the file ranges of each context and subtract the ranges
owned by the other contexts, then create one buffer per range. Weights that
are mapped in more than one context (e.g. token_embd.weight) keep being part
of the range of every context that uses them.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* llama : enable prefetch on macOS
The prefetch hint of the tensors marked as prefetchable is a no-op on macOS,
so their pages are faulted in one by one while the graph runs. madvise
(MADV_WILLNEED) behaves as expected here, so add the platform to the guard.
With the lazy per-layer token embeddings of Qwen3.8-Flash-Next IQ4_NL read
from a cold file cache, prompt processing goes from 356 / 375 / 475 t/s to
735 / 749 / 788 t/s for pp512 / pp2048 / pp4096.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* llama : use a plain buffer list per context
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* graph: skip the LM head for pooled embeddings
A context that extracts pooled embeddings outputs one vector per
sequence, but every model still expanded its LM head, so the graph
computed n_vocab logits per token and the context kept a host buffer
for them.
The models now only declare t_logits, and set_outputs() expands it
unless the context extracts embeddings with a pooling type other than
NONE. Since the toggle changes the graph shape for a pooled context,
set_embeddings() schedules a re-reserve in that case.
* graph: drop the redundant t_embd expand
Address review from @CISC: with embeddings on, build_pooling() already
expands its output for every pooling type, and a decoder graph without
embeddings does not read t_embd, so set_outputs() only expands
t_logits.
* graph: add build_logits() for the LM head output
Address review from @ggerganov: the LM head is expanded by a dedicated
build_logits(), called from llama_model::build_graph() like
build_pooling(), and set_outputs() only marks the outputs again.
* graph: call build_logits() before build_sampling()
Address review from @ggerganov: build_sampling() reads t_logits, so the
LM head is built first. A pooled context then skips the sampling layers
too, since it has no logits to sample from.
* nit
* tests : consolidate the context state tests into test-llama-context
test-save-load-state, test-recurrent-state-rollback and
test-state-restore-fragmented check the same object from three places:
the KV cache and the recurrent state of a llama_context, saved, loaded,
copied, rolled back. Each had its own main, its own loop over the
models, its own copy of the token and the comparison helpers, and a run
of all three loaded the same models several times.
They are one binary now, and the case is the unit of the test:
tests/llama-context/test-case.h the interface a case implements
tests/llama-context/cases-kv.cpp the cases of the KV cache
tests/llama-context/cases-rs.cpp the cases of the recurrent state
tests/llama-context/test-util.cpp tokens, generation, comparison
tests/llama-context/test-runner.cpp the models of a case, on threads
tests/llama-context/test-log.cpp the log file of a model
A run is a table: a row per model, a column per case, a cell holds the
verdict of that pair. The models load on several threads (-j, --threads,
min(cores, 4) by default), the table is printed when it is complete and
the log of a model that failed is printed after it, in the order of the
rows. A case returns a verdict and the reason for it, and the runner is
the only one who reports - the test does not use tests/testing.h, a run
over 130 models is a matrix of verdicts and not a tree of subtests.
Staging no longer travels through the environment: llama_model_set_attn_rot
replaces the LLAMA_ATTN_ROT_DISABLE variable, which a model loaded in one
thread used to change for the models the other threads were loading, and
llama_memory_get_buffers is added for the cases that copy the buffers of
a memory. common_log_set_stdo keeps the console quiet while the table is
being built.
ggml_aligned_malloc allocates with posix_memalign when the thread
sanitizer is on. On macOS it allocates with the Mach VM, which the
sanitizer does not intercept, so it never sees a page range change hands
and reports the first write into recycled memory as a race - 30 of them
in this test, and every one of them false.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* cont : run single-threaded by default
* cont : try to build the tests on all platforms
* cont : trim down some body fat
* cont : fix
* cont : fix the fix
The no-cache path builds a [n_tokens, n_tokens] mask and passes K and V
unpadded, so FLASH_ATTN_EXT sees K->ne[1] == n_tokens. The CUDA kernels
for head size 512 require a multiple of FATTN_KQ_STRIDE, so the op falls
back to the CPU for most lengths, and the -fa auto probe (1 token)
disables flash attention for such models.
Pad the mask like llama_kv_cache::get_n_kv and pad K and V with zeros up
to the KV dimension of the mask. clef builds its own causal mask and
pads it the same way.
Add an optional ggml_backend_sched_realloc_callback that user code registers with
ggml_backend_sched_set_realloc_callback to be notified when the buffers reserved for a
graph are not sufficient and have to be reserved again. The callback reports whether the
backend assignments and the graph size changed since the previous scheduling.
llama_context registers the callback when creating the scheduler (wrapped into the new
llama_context::sched_new helper) and treats the reallocations that follow a change of the
backend assignments as expected, counting the rest by graph size - unchanged or changed
from the previously scheduled graph. Both counters are reported on one line in the perf
stats.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* model : use exact GELU for ModernBERT encoders
Assisted-by: Codex
* model : keep tanh GELU aliases on ggml_geglu
Assisted-by: Claude Opus 5.5
* model : map gelu_python to ggml_geglu_erf
Assisted-by: Claude Opus 5.5
The reserve builds n_outputs_max_per_seq sampling chains per sampler,
while a decode built one per output row, so the graph changed its
topology after the reserve and GGML_SCHED_NO_REALLOC builds aborted on
the next same sized graph. Every sampler now builds
n_outputs_max_per_seq chains, the ones without a row of the ubatch on
the padding row and not selected, and graph_max_nodes counts them.
* meta : handle views of tensors allocated on the host
A view shares the memory of its view_src, so ggml-alloc never allocates a view in
the buffer of the split it lands in - the scheduler copies the source into the
split and the ops that use the view read that copy. The view node itself is a noop
and does not need a split of its own, but the meta backend asserted when one was
left inside a meta split:
- ggml_backend_meta_get_split_state() dereferenced tensor->buffer->context
- the graph rebuild mapped every node with ggml_backend_meta_buffer_simple_tensor()
Accept such nodes when they are views of host tensors, which also generalizes the
previous s_copy_main workaround. This fixes the assert hit by KV cache views when
using --split-mode tensor with partial offload.
Assisted-by: pi:llama.cpp/Qwen3.8-Flash-Next
* archs : re-enable sm tensor for K2 Horizon
* cont : add TODO and reference
* model : support classifier_activation for rerankers
Assisted-by: Claude Opus 5.5
* model : map classifier gelu to gelu_erf and accept tanh
Assisted-by: Claude Opus 5.5
* model : default act_cls to tanh, ModernBERT falls back to gelu_erf
Assisted-by: Claude Opus 5.5
* model : add LiquidAI/d1-omni-600M decision model
Assisted-by: Claude Opus 5.5
* mtmd : keep conformer GLU sigmoid on CUDA
Assisted-by: Claude Opus 5.5
* server : take d1omni audio through images and input_audio, scope memory-less lfm2 to non-causal
Assisted-by: Claude Opus 5.5
* common : rename decision type d1omni to lfm2-d1-omni, server : make images an alias of files
Assisted-by: Claude Opus 5.5
* llama: share the nextn tensor flags between models
Follow-up of the TODO in glm5-next: move the trunk-only and MTP-only
detection that each model copied into a nextn_flags helper of
llama_model_base. It probes the first trunk layer and the first NextN
layer, and adds TENSOR_SKIP when MTP is not loaded. qwen4exp probes
hc_attn_norm since it has no attn_norm.
deepseek4, nemotron-h, qwen35, qwen35moe, qwen3next and qwen4exp now
also accept a trunk-only file, like the other models.
* llama: avoid capturing structured bindings in the nextn flags
Lambdas that capture structured bindings need C++20, and GCC 15
rejects them under -Werror, so the models read the trunk and MTP
flags into plain variables.
* llama : add GLM5-Next NextN (MTP) graph
Build the GLM5-Next multi-token-prediction head as graph_mtp: the NextN block
embeds enorm(tok)+hnorm(h) through eh_proj, runs one plain DSA layer and the
shared lm_head, reusing the trunk's builders through the no_build tag ctor.
llama_memory_recurrent also tolerates a partial seq_rm when the context holds
no recurrent layers, which is what the MTP draft context needs.
Assisted-by: Claude
* llama : glm5-next: skip dead compute in headless NextN forwards
A NextN forward with no output rows (the MTP catch-up and the draft-context
prefill) persists only through its cache writes, so the headless graph keeps
the MLA latent, indexer key|gate and pooled-key writes and drops the query
path, the indexer selection, the attention body, the FFN and the LM head. The
4-token catch-up falls from 6.9 ms to 0.33 ms of kernels; the greedy output
hashes and the draft acceptance are unchanged.
Assisted-by: Claude
* llama : glm5-next: fix NextN extraction contracts and shared-tail rollback
Three fixes from the architectural review. The headless graph prune now also
requires that no unmasked nextn extraction is live, because that mode reads
n_tokens hidden rows regardless of the logits flags. Masked extraction
publishes the hidden rows gathered by the output ids, so a batch whose output
flags are not a prefix exports the right rows. A partial recurrent rollback
whose tail cell is shared with another sequence is now rejected instead of
silently moving that sequence's tail.
Assisted-by: Claude
* llama : glm5-next: tidy comments in the MTP changes
Assisted-by: Claude
* llama : glm5-next: crop the MTP graph to the output rows instead of pruning it
Replace the headless NextN prune with the crop pattern the other MTP
graphs use: gather the attention output and the block input at the
output ids before the position-wise FFN and the shared head. A NextN
forward with no output rows (the MTP catch-up and the draft-context
prefill) then runs the FFN and the head over zero rows. The 4-token
catch-up falls from 6.9 ms to 2.9 ms of kernels; greedy output hashes
are unchanged.
Assisted-by: Claude
* glm5-next: use the nextn crop helpers in the MTP graph
Replace the local crop condition and the masked select of t_h_nextn
with crop_before_nextn and crop_after_nextn, so the MTP graph narrows
its rows the same way as the main graph and the other models.
Describe the shared cell and empty filter branches of the recurrent
partial rollback.
* glm5-next: load MTP-only and trunk-only GGUF files
Make the trunk tensors optional when the file only holds the NextN
layer, and the NextN tensors optional when the file only holds the
trunk, so the split MTP GGUF loads as a draft model.
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Pascal <admin@serveurperso.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sampling : use greedy selection for eligible temperature-zero chains
Assisted-by: OpenAI Codex
* Apply suggestion from @ggerganov
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* sampling: simplify zero-temperature greedy eligibility
Allow the same greedy selection on CPU and grammar/reasoning-budget paths.
Keep distribution sampling for dynamic temperature and requested probabilities.
Cover the common sampler selection and probability behavior in the existing sampler tests.
Assisted-by: OpenAI Codex
* sampling : use greedy selection after final top-k with k=1
Assisted-by: OpenAI Codex
* cont : clean-up
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Xue ZhenliangandSigbjørn Skjæret
Daniel Han
Mikhail Podvitskii
Aman Gupta
Gaurav Garg
Xuan-Son Nguyen
Kartik Gulia
Karthik VijayanandKarthik Vijayan
Simone IacominoandSigbjørn Skjæret
Adrien Gallouët
Sandro Hanea
Adam CaldwellandAdam Caldwell
Oliver Simons
Animesh
Muhammad SaadandSigbjørn Skjæret
bri-prismandGeorgi Gerganov
bosh
Hrishith Thadicherla
Tarek Dakhran
