The old note guessed that the hyper-connection keys never reach the file.
They do: dumping the gguf_context handed to llama_model_init_from_user
shows both among its 67 KVs, and the loader still reports one missing.
The indexer cache found its own slots, independently of the attention
cache. Both are the same size and see the same ubatches, so in a
straight-through prefill they agree, which is why every fixture and every
single-shot parity run passed. They drift once the context is being
rewritten between turns, and then the QSA top-k indices, which are applied
against the attention mask, point at the wrong cells.
The seven-turn chat test caught it on the third turn: llama-server aborted
on the assertion that the two caches report the same n_kv.
The cache is a side buffer addressed by the attention cache's cells, so it
now takes that cache's slot layout instead of computing one. Applying that
layout also marks its cells identically, so the two agree cell for cell by
construction rather than by coincidence, and the assertion can no longer
fire.
Inert where the caches already agreed: test-llama-archs green at 126 archs
and 0.00e+00, and the 4096-token tiny fixture is unchanged at max logit
delta 0.0.
The full-attention layers of this model do not attend to everything. An
indexer scores one mean-pooled key per block of compress_ratio tokens and
keeps a budget of the best blocks, plus the tail of tokens that do not yet
form a complete block. Below indexer_top_k + compress_ratio - 1 cached
tokens every block fits in the budget, so the result is exactly dense.
What is reused rather than rebuilt:
- the mask machinery. build_attn's DSA overload already turns a list of
token indices into a KQ mask via ggml_set_rows, so that block is lifted
out verbatim into build_attn_mask_top_k and shared with a new overload
on llm_graph_input_attn_kv. DSA's node sequence is unchanged; the new
overload exists because llama_kv_cache_dsa assumes MLA and cannot be
dropped into a hybrid model.
- the indexer key cache, which is the optional third cache added to
llama_memory_hybrid in the previous commit. It holds raw keys, because
pooling happens before the norm and the rotation.
The graph expands block scores rather than block indices: giving every
token of a block its block's score needs only a gather, where expanding
indices would need an integer multiply-add that ggml has no op for. Since
the budget is a whole number of blocks and a block's members tie exactly,
the cut still lands on a block boundary.
Everything that depends on cache layout is computed host-side in
set_input_qsa. Blocks are cuts of the position line rather than of the cell
array, so nothing assumes the cache is contiguous.
Measured on the tiny fixture against vLLM, comparing the selected token
indices directly rather than the logits:
below the budget selection identical, and 1024-token logits are
bit-identical to the pre-QSA dense path
above the budget mean jaccard 0.975
The direct index comparison is what made this correct. The reference
rectifies each head's dot product before summing over heads, which an
earlier reading of it had missed; on logits alone the resulting port looked
fine, because on a randomly initialised fixture the known-correct dense
path already disagrees with vLLM by more than the bug did. Comparing the
indices showed 0.794, and fixing the ReLU moved it to 0.975.
Groundwork for qwen4exp's QSA sparse attention. Its indexer needs a per-token
key history for the full-attention layers, but a hybrid model cannot use
llama_kv_cache_dsa: that class derives from llama_memory_i rather than
llama_kv_cache, and llama_memory_hybrid constructs its attention cache
directly. No existing architecture pairs recurrent state with a sparse
indexer, so there was nothing to reuse wholesale.
llama_memory_hybrid therefore gains a third, optional cache, shaped the same
way llama_kv_cache_dsa shapes its lightning-indexer cache: a copy of hparams
with n_head_kv forced to 1 and n_embd_head_k_full set to indexer_head_size.
It is built only when a filter_idx callback is passed, which defaults to
nullptr, so every existing architecture gets exactly what it got before. The
per-sequence operations and the batch preparation forward to it under a null
check, matching how the DSA cache prepares its two caches over the same
ubatches.
test-llama-archs passes all 124 architectures at 0.00e+00, including the 12 in
the hybrid family that share this code. The qwen4exp fixtures are unchanged:
same logits against vLLM, and chunked evaluation still bit-identical to
single-shot.
build_rs writes into the state tensor in place, zeroing one row and copying the
carried-over states, so calling it twice for the same layer let the second call
clobber the first write-back. The PLE layer is also a delta-net layer, so that
is exactly what happened: both convolutions gathered the same row. They now
share a single gather per layer.
The earlier claim that the conv state was carried correctly was tested on a
fixture whose conv weights are zero, where the branch contributes nothing and
chunking matches trivially. Re-running with non-zero conv weights showed the
divergence, growing with the number of ubatch boundaries: 97.1% top-1 at one
boundary down to 90.2% at seven. With the shared gather it is bit-identical to
the single-shot run at every chunk size tried, 512, 128 and 64, with a maximum
logprob deviation of exactly zero over 1023 positions. The delta-net-only model
stays bit-identical too, so nothing regressed there.
Also derive the delta-net conv channel count the way load_arch_tensors sizes
wqkv instead of from ssm_d_inner. The two agree for this model, but n_embd_r()
only bounds the row and the convolution has to match the tensor feeding it.
test-llama-archs previously aborted on this architecture and took every later
architecture with it. qwen4exp is marked MoE-only, given the hyper-connection
keys and an ssm_d_inner consistent with its tensor derivation, and skipped for
now: the hyper-connection keys written by get_gguf_ctx are not reaching the
synthesised file, which needs a separate look. The suite completes again, 124
architectures at 0.00e+00.
The PLE depthwise conv was zero-padding on the left, which is only right for a
prefill that starts at position 0. Decode and chunked prefill saw a truncated
history for the first (kernel-1)*ngram_size positions of every ubatch.
The PLE module sits on a layer that is also a delta-net layer, so both need a
conv history in the same recurrent row. Rather than plumb a per-layer state
size through build_rs and build_conv_state, the row is widened once and each
convolution addresses its own slice through a local helper. n_embd_r() gains
the extra span, which is zero for every other architecture because it is
derived from ple_n_heads.
Verified by feeding the same 1024 token sequence in chunks instead of one
shot: at 64 tokens per decode the logits are bit-identical to the single-shot
run, 1023 of 1023 top-1 and a maximum logprob deviation of exactly zero. At
one token per decode they differ slightly, but the no-PLE model differs more
under the same test (94.6% against 97.1%), so that is the usual gemv-versus-
gemm accumulation difference and not the state.
The conv branch is also no longer unverified. With non-zero conv weights the
port sits 6.3 points of top-1 below the numerical floor, the same distance as
with the weights zeroed and as the model with no PLE at all, so the branch
adds no error of its own.
test-llama-archs passes every existing architecture at 0.00e+00, including the
delta-net models that share this code path.
Adds the per-layer embedding: a custom I32 graph input hashes each token with
its ngram_size-1 predecessors host-side and the result is a plain row gather
over the shared table, the same shape gemma3n's per-layer embedding uses. The
hash has to run on the host because the splitmix64-derived multipliers reach
2^45, so the products need 64-bit integers and an xor, neither of which ggml
has.
Predecessors that fall outside the ubatch come from a small per-sequence
history on the model, mirroring the per-request ngram_context the reference
carries. It is only trusted when contiguous with the incoming position, so a
fresh prompt or a rewound cache falls back to EOS padding rather than hashing
against stale tokens.
The depthwise conv is written out as a sum of shifted, per-channel-scaled
copies rather than through ggml_conv_1d_dw, which carries a correctness
warning upstream.
Verified two ways. The row indices match a transcription of the reference's
tensor formulation exactly, 1024 of 1024 rows, including sequences with EOS
tokens sprinkled through them to exercise the segment reset. Separately, with
PLE placed on layer 0 so its input is just the token embedding, ple_embd and
ple_gated_value match a PyTorch computation from the same checkpoint to every
printed digit.
End to end over 1023 scored positions the port sits the same distance from
vLLM with PLE as without it, 6.3 points of top-1 against 6.0, so PLE costs no
accuracy relative to the rest of the model. That common offset is vLLM's bf16
activations, which cannot be removed: its QSA kernel refuses float32.
Two bugs found along the way, both caught by the row-index check. The history
was read and updated in the same pass, so a token early in a ubatch could pick
up an earlier token of that same ubatch as prior context; it is now snapshotted
first. And an EOS token was cutting its own context, where the reference takes
the last EOS strictly before the position, so a boundary only hides tokens from
the positions after it.
Known gap: the conv carries no state across ubatches, so it is exact only for a
prefill that starts at position 0. Chunked prefill and decode need the conv
state wired into the recurrent memory, and the conv branch itself is still
numerically unverified because the fixture zeroes its weights.
Implements the decode graph for Qwen3.8-Flash-Next: the hyper-connection
residual stream, gated delta net layers, the MoE block with its gated shared
expert, and dense full attention. The QSA indexer and the PLE n-gram embedding
are not wired up yet and land in later commits.
Hyper-connections are implemented here rather than shared with deepseek4.cpp.
The two formulations agree on the [n_embd, hc, n_tokens] layout and little
else: DeepSeek-V4 mixes with a full-rank projection and Sinkhorn-normalises
it, whereas this model uses a low-rank down/silu/up sigmoid gate and collapses
by a plain mean. Only the ~10 line stream mean is genuinely common, so sharing
would mean touching DSV4's hot path and its three fused CUDA ops to reuse very
little. What is reused is the substantive part: the LLM_KV_HYPER_CONNECTION_*
keys, the n_embd_out_impl wide-residual support already in the loader, and the
layout convention.
Also allows a checkpoint to carry no PLE layers at all, which makes it
possible to bring the graph up and validate it in stages.
Validated against vLLM, the only working reference implementation. On a
scaled-down model with an init scale large enough to give non-uniform logits,
agreement with vLLM sits at the numerical noise floor: llama.cpp f32 against
its own bf16 gives 84.3% top-1 agreement over 255 positions, and this graph
against vLLM gives 85.1%. The comparison was calibrated by seeding three
deliberate bugs (silu instead of sigmoid on the delta net gate, dropping the
1/hc scale in the mix, dropping the 2x in the combine); each drops top-1 to
between 0% and 11%, an order of magnitude below the floor.
Adds LLM_ARCH_QWEN4EXP with its hparams and tensor loading. The graph
comes in the next commit; this makes the model load and report correct
metadata.
- hyper-connections set n_embd_out_impl = hc_count * n_embd, so the
residual stream is 4x wide and there is no output_norm: the final
mixer's hc_norm is the last norm in the model.
- registered as hybrid and given the same recurrent/attention memory
filters as Qwen3-Next and Qwen3.5.
- reuses the existing indexer, per_layer_token_embd, SSM and
compress_ratios keys as-is.
- the PLE table row count is read back from the file rather than
recomputing the vocab padding rule.
llama-model-loader gains UINT64 array support. That branch previously
threw, so no existing caller changes behaviour; it is needed because the
PLE hash multipliers do not fit in int32.
Adds the GGUF-side plumbing for HF model_type qwen4_exp:
- MODEL_ARCH.QWEN4EXP plus tensors for the low-rank hyper-connection
variant (hc_*_norm/down/up/inject) and the PLE n-gram hash embeddings.
The DeepSeek-V4 hc_*_fn/base/scale tensors are a different
parameterisation, so these are separate entries rather than reuse.
- Reuses the existing indexer, per_layer_token_embd, SSM and
compress_ratios keys unchanged.
- conversion/qwen4exp.py inherits the Qwen3.5 linear-attention V-head
reorder and interleaved mrope, concatenates the 128 PLE embedding
shards, and splits index_qk_proj into separate indexer q/k tensors.
The PLE hash multipliers reach ~2.4e13. prepare_tensors() casts every
non-float dtype to float32 before modify_tensors() runs, and GGUF array
writes infer INT32 from Python ints, so both paths are bypassed: the
constants are read from the pre-cast lazy tensors and written as
explicit UINT64 arrays.
Additive only; no existing arch changes behaviour.
* ci : inline UI version resolution into ui-build.yml
* ci : build UI once and reuse the artifact in release jobs
Server jobs now extract the ui-build artifact into tools/ui/dist instead of npm-building the UI. Also removes the get-version job and the no-op -DHF_UI_VERSION flags.
Assisted-by: pi:Kimi-K3
* ui : disable the npm UI build by default (LLAMA_BUILD_UI=OFF)
The flag now only controls building the UI from source via npm. The UI
is still embedded by default from local tools/ui/dist or the prebuilt
download (LLAMA_USE_PREBUILT_UI=ON). CI jobs no longer npm-build the
UI; server-sanitize does not need node anymore.
Assisted-by: pi:Kimi-K3
* ci : rename the ui-build artifact to llama-ui.zip
Consistent with the other artifact names in the Actions summary.
Assisted-by: pi:Kimi-K3
* ci : clarify the windows artifact merge in release.yml
The windows-cuda/vulkan/sycl jobs build only the backend library;
llama-server (with the embedded UI) is injected into their zips from
the windows-cpu package during the release. State this in the job
comments and use accurate wording in the merge step.
Assisted-by: pi:Kimi-K3
* cuda: unblock mmq for MoE on sm_60
* cuda: duplicate mmq-config-pascal for dp4a and older
* cuda: reduce occupancy on non-dp4a pascal for Q2_K, Q4_K, Q5_K, Q6_K
This commit contains a fix for the conversion of NVIDIA Nemotron 3.5
Lightning which currently incorrectly converts when using a transformers
version later than 5.5.1.
When converting using [convert](https://github.com/ggml-org/convert) the
transformers version is 5.13.1 and this produces the following:
```console
WARNING:gguf.gguf_writer:Duplicated key name 'nemotron_h_moe.attention.head_count_kv', overwriting it with new value [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] of type ARRAY
```
This does not happen with transformers 5.5.1. The reason seems to be
that the configuration is different in later versions, for example when
using 5.13.1 the configuration block looks like this:
```console
transformers 5.13.1
raw has layers_block_type: True
autoconfig has layers_block_type: True
autoconfig layers_block_type: [
'linear_attention',
'moe',
'linear_attention',
'moe',
'linear_attention',
'full_attention',
'moe',
...
]
```
And with 5.5.1 we get:
```console
transformers 5.5.1
raw has layers_block_type: True
autoconfig has layers_block_type: True
autoconfig layers_block_type: [
'mamba',
'moe',
'mamba',
'moe',
'mamba',
'attention',
'moe'
...
]
```
In our conversion script we only match for attention, not full attention
which is causing this issue.
With the changes in this commit the output with transformers 5.13.1 will
be:
```console
(venv) $ gguf-dump models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.gguf | grep head_count_kv
INFO:gguf-dump:* Loading: models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.gguf
29: [INT32] | 52 | nemotron_h_moe.attention.head_count_kv = [0, 0, 0, 0, 0, 2, ...]
```
Resolves: https://github.com/ggml-org/llama.cpp/issues/27718
Refs: https://github.com/ggml-org/convert/actions/runs/32949047680/job/98116096069#step:5:2391
* metal: WIP chunked SSD SSM_SCAN kernels for multi-token prefill
* metal: drop scalar SSD path; MMA + sequential tail
* drop WIP ssm scan test noise
* remove state_from_dst and rename CS and NSG constants
* remove unrelated added whitespace padding
* added clarity to mma_tokens calculation
* added clarity to use_mma bool checks
* added comments to metal ssd op constants for clarity
* reserve K tokens for sequential kernel rollback snapshots
* reset concurrency between mma and seq tail
* remove print args no longer used
* fixed comment to no longer point to specific line
* add FC_SSM_SCAN so seq path skips token offlset unless it's mma tail
* added changes to new ssm.metal for rebase after ggml-metal.metal refactor
* specialize ssm_scan tail with a template instead of a function constant
---------
Co-authored-by: dpantaleoni <[email protected]>
Co-authored-by: forforever73 <[email protected]>
* rpc: support apple RDMA as an RPC transport
* remove set_tensor micro optimization, rpc socket pinning per CR
* remove transparent reconnect
* trigger apple builds on RPC changes
---------
Co-authored-by: Ryan Churaman <[email protected]>
* devops: use GGML_NATIVE=OFF for OpenVINO
Same as in other Dockerfiles.
Should fix#23100
* enable backend dl and cpu all variants
---------
Co-authored-by: Sigbjørn Skjæret <[email protected]>
* server: fix tool calls getting silently stripped with --prefill-assistant
Last assistant carries tool_calls + --prefill-assistant is on → request
flips into continuation mode, add_generation_prompt forced off, tail
rebuilt from reasoning_content + content only. Tool calls just vanish.
- Auto-continuation now skips trailing assistant msgs that have tool calls
- continue_final_message on those throws a clear error instead of
silently corrupting the prompt
- Regression tests included, red before / green after
Fixes#27588
Developed with AI assistance, disclosed per the contribution policy.
* server : address review: fail on prefill-assistant + trailing tool_calls
Move validation into oaicompat_chat_params_parse (next to the existing
two-or-more-assistant check) and remove it from common_chat_templates_apply,
which has no precedent for validation. Drop the regression tests.
Per review: --prefill-assistant with a trailing assistant message
containing tool calls is not supported and should fail loudly.
* add ccache-buckets action
* use ccache-buckets
* only save on master
* install python3-venv for hip
* add jq and python3 for cuda
* only delete caches older than 5 minutes
* metal : null-check ggml_metal_buffer_init result to avoid OOM crash
ggml_backend_metal_buffer_type_alloc_buffer used the result of
ggml_metal_buffer_init without checking for NULL. ggml_metal_buffer_init
returns NULL when the underlying Metal allocation fails (e.g. an
out-of-memory condition), and the following ggml_metal_buffer_is_shared(res)
call dereferences it, turning a recoverable allocation failure into a hard
crash (EXC_BAD_ACCESS). This is easy to hit on memory-constrained devices
such as iOS when a model/context exceeds the available Metal budget.
Log the failure using the existing GGML_LOG_ERROR convention and return
NULL so the allocator surfaces a diagnosable error up the stack instead of
crashing.
* cont : fix log
---------
Co-authored-by: Georgi Gerganov <[email protected]>
* grammar : accept "\-" escape in character classes
gbnf_escape_char_class() escapes '-' as "\-" but parse_char() rejected
that escape, so generated tool-call grammars failed to parse.
Assisted-by: Claude Code <[email protected]>
* tests : add parser test for "\-" in char classes
Assisted-by: Claude Code <[email protected]>
* tests : add integration test for "\-" in char classes
Assisted-by: Claude Code <[email protected]>
* tests : drop integration and parser tests
* metal : per-device tuned (Q, NE) for flash-attn vec (#25750)
* rebase Q-generic FA vec body from 01dc93607 (#23114)
* add 53 f16 (Q,NE) flash-attn vec instantiations (vec 80 -> 133)
* add FA vec (Q,NE) tuning table + dispatch wiring + SMEM cap fallback
* add FA vec (Q,NE) perf sweep
* fill tuning result
* fold family table into a per-family representative SKU
* refactor tuning result format
* extend FA vec tuning to quantized KV caches
* sync fa vec tuner bucketing with runtime, use pointwise tuning regret
* update tuned table
* format and cleanup
* prefix fa_vec tuning procs with ggml_backend_metal_tuning_, drop unused fa_vec_override_active
* add device id -> token lookup for the offline tuning tool
* add ggml-metal-tuning skeleton
* add op-agnostic perf cell + median timing for the tuner
* add FA-vec graph build + tensor init to the tuner
* tools : add FA-vec (Q,NE) sweep, compression and table emit
* cool down and re-measure the dirty window on thermal drift
* test-backend-ops : replace the FA vec tune mode with a bounded (Q,NE) slice
* tools : document the Metal tuner, point the table comment at it
* abort on unknown KV type, single-source fa_vec_legal_ne
* cleanup
* honor -o in the FA vec (Q,NE) slice
* retune FA-vec (Q, NE) under a pointwise no-harm gate
* cont : add fa-vec tunings for M1 Pro, M2 Ultra, M5 Max
---------
Co-authored-by: Georgi Gerganov <[email protected]>
* metal : per-op source split + parallel compile (#24021)
* preliminary extract common header
* op source split
* split metallib into 8 libs && load in parallel
* derive kernel->library routing from functionNames
* x-macro lib list + underscore filenames, dedup QK_NL, MRC fixes
* op source split 8 to 20
* improve robustness of source fallback
* clean up
* change bool -> atomic_bool
* only prepend headers that source actually includes
* no semaphore, use GCD global queue
* dedup library compile path, fix NSError lifetime, rename gla
* relocate upstream concat/rope_back/repeat kernel changes into split files
* move ggml-common.h from common.h into dequantize.h to shrink binary size
---------
Co-authored-by: lvyichen <[email protected]>
* metal: add col2im_1d op (f32/f16/bf16) (#25176)
* metal : add set_rows with src0 f16 (#25434)
* metal : add CONV_2D_DW (depthwise convolution) support (#21565)
* metal : add Q2_0 support (#25419)
* metal: fuse snake activation (mul, sin, sqr, mul, add) (#25459)
* ggml-metal: FWHT kernel for metal backend (#25924)
* metal : port new kernels into the split sources
Move the kernels added on master after the split (lightning indexer,
DSv4 hyper-connections, silu_back, f16 bin ops, TQ2_0, the flash-attn KV
dequantization pass, rope offset/inplace, ssm_scan rollback, packed q8_0
dequantization and the tensor-API mat-mat K clamp) into the corresponding
kernels/*.metal sources. Copied verbatim, no functional change.
---------
Co-authored-by: lvyichen <[email protected]>
Co-authored-by: Georgi Gerganov <[email protected]>
`repetition_penalty` is standard HF key for repetion penalty.
Currently, only `penalty_repeat` is mapped, read `repetition_penalty`
and map it to `metadata.sampling_penalty_repeat`.
* ci : apply ccache-clear with older/min/dry-run to all ccache jobs
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : install gh in ccache-clear if missing (container jobs)
The ccache-clear action relies on the gh CLI, which is not present in
container-based jobs. Install it on demand so those jobs can clear caches.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : install gh via apt repo in ccache-clear
The install.sh script used previously is no longer served (404). Switch to
the official GitHub CLI apt repository, which is still available.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : pass --repo to gh cache commands in ccache-clear
In container jobs gh cannot auto-detect the repository from git, so
gh cache list/delete fail with 'failed to run git: not a git repository'.
Pass the repository explicitly via --repo using GITHUB_REPOSITORY.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : drop -new suffix from vulkan ccache key
The -new suffix was only needed to force a fresh cache. With
ccache-clear now evicting stale caches, the original key can be used
again. The old ccache-vulkan-ubuntu-24.04-arm-new entries still match
the ccache-clear key prefix and are cleaned up automatically.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : fix ccache-clear date parsing on macOS (BSD date)
macOS ships BSD date, which has no -d option. The older cutoff check
was silently disabled there: 'date: illegal option -- d' errors in the
log and the loop was only stopped by the min limit, risking deletion
of caches not older than the cutoff (e.g. saved by a concurrent job).
Parse the ISO-8601 timestamps with GNU date when available and fall
back to BSD date otherwise (TZ=UTC, fractional seconds dropped).
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : extract ccache-clear logic into scripts/ccache-clear.sh
The composite action now consists of a dedicated step that installs the
GitHub CLI when missing (e.g. in container jobs) and a thin step that
calls the new script. The script follows the make-release-checks.sh
conventions (usage/env header, set -euo pipefail, CLI flags) and only
checks that gh is available. The action inputs are unchanged, so the
workflow steps are untouched.
Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731
* ci : remove unused apple ccaches
* DSV4: sm tensor
* set coarser granularity for head splits
* fix dspark
* add model saving for dsv4 + allow dflash to return on specific device
* add comment about dsv4 seq_rm
* simplify
* add shared expert delayed allreduce
* remove special test for dsv4
* readme : update links
* readme : update maintainer PRs list
Add the new members of the `ggml-org` `maintainers` team to the
author filter of the maintainer PRs link (nikwen, marty1885,
Titaniumtown), keeping the canonical team ordering. The list now
matches the team exactly (35 members).
Assisted-by: pi:llama.cpp/Qwen3.8-27B
Daniel Han
Aleksander Grygier
David Friehs
Sigbjørn Skjæret
Daniel Bevenius
Max Krasnyansky
Jonathan Clohessy
Mario Limonciello
Ryan CandRyan Churaman
krzsztfandSigbjørn Skjæret
Kyozzz
Aldehir Rojas
Yuri KhrustalevandGeorgi Gerganov
Saad Ali
Neo Zhang
fairydreamingandStanisław Szymczyk
YiChen LvandGeorgi Gerganov
Tarek Dakhran
jacekpoplawski
Xuan-Son Nguyenandrkfg
Prabhsimran Singh
Aman Gupta