* 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>
* feat(convert): Attempt to come up with 256 single-token, semantic option markers
Each switch model needs to identify a collection of single-token option
markers that can be mapped to their respective logits for the output
probability distribution. The simple upper/lower/numbers set only yields 66
unique markers, but the model can support up to 255. The challenge is that
which combinations of characters result in single-token markers is model-
specific, so we do a discovery exercise at conversion time by looking
through the specific model's vocab for single-token sequences of 1+ plain
letter strings (eg `A`, `AA`, `BB`, etc).
Branch: GraniteSwitchDecisionFullOptions
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Note granite-switch with 256 option markers
Branch: GraniteSwitchDecisionFullOptions
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* style: Remove extra newline
Branch: GraniteSwitchDecisionFullOptions
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert): Use the computed labels in the template so c++ side is source of truth
Branch: GraniteSwitchDecisionFullOptions
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Use LEV case for GRANITE_SWITCH w/ computed labels
Branch: GraniteSwitchDecisionFullOptions
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Consolidate g-s into 255 bucket
Branch: GraniteSwitchDecisionFullOptions
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert): Add decision.chat to support decision models that can also chat
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(convert): Add conversion for Granite Switch as decision model
The template follows from the usage sample:
https://huggingface.co/ibm-granite/granite-switch-4.1-8b#guardian-and-decision-side-by-side
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(common): Add support for chat+decision models in common
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(common): Add granite switch decision model enum
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(server): Plumb through decision_chat bool to support decision+chat models
Branch: GraniteSwitchDecision
AI-usage: full (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat(server): Add Granite Switch decision support
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Update server README for granite switch decisions / chat+decisions
Branch: GraniteSwitchDecision
AI-usage: draft (Claude + Sonnet 5.5)
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* Revert "feat(convert): Add decision.chat to support decision models that can also chat"
This reverts commit 952ef567647e53ac9beda7a1f79d6927240d068d.
* Revert "feat(common): Add support for chat+decision models in common"
This reverts commit 93ed782e3a8b2fd5968732c6525c7ad29ad4bea2.
* Revert "feat(server): Plumb through decision_chat bool to support decision+chat models"
This reverts commit 97e9af60ddaccbd82f1eb0a488adff43b16ee095.
* fix: If Granite Switch supports decisions, it always supports text too
Branch: GraniteSwitchDecision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* docs: Remove reference to decision.chat in server README
Branch: GraniteSwitchDecision
AI-usage: none
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Use the instance's is_mistral_format flag when DeepseekV2Model reloads
configuration, allowing Mistral MoE checkpoints to read params.json.
Fixes#29899
Signed-off-by: aiqubits <aiqubits@hotmail.com>
* 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>
* 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>
* 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
* model : add LiquidAI/d1-3b decision model
mtmd : read LFM2 image resize algo from GGUF
Assisted-by: Claude Opus 5.5
* common : rename decision type d1 to lfm2-d1
Assisted-by: Claude Opus 5.5
* convert : Fix token configuration for PLaMo-3
The reasoning and tool calling tags in PLaMo-3 consist of three tokens
each. For example, for reasoning:
* reasoning start: `<|plamo:begin_`, `think`, `:plamo|>`
* reasoning end: `<|plamo:end_`, `think`, `:plamo|>`
Registering `<|plamo:begin_`, `<|plamo:end_`, and `:plamo|>` as
`USER_DEFINED` so that they are parsed correctly.
Also PLaMo-3 models use <|plamo:tag|> as EOT, while PLaMo-2 models
use <|plamo:op|>.
Take EOT token as a parameter and look it up so that PLaMo-2 and
PLaMo-3 can use their appropriate ones.
* use NORMAL instead of USER_DEFINED
* vocab : implement PLaMo-3 tokenizer pre-segmentation
The PLaMo-3 tokenizer inserts hard boundaries before running the Unigram
DP, around <|plamo:...|>-looking text, and around runs of at least 4
identical characters or 2 spaces. Without them llama.cpp tokenizes code
indentation and repeated punctuation differently from the reference.
Reproduce the two re.sub() passes in llm_tokenizer_plamo2 by encoding
each segment independently.
* add vocab type "plamo3"
* Update src/llama-vocab.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* misc change
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* model: K2 Horizon gguf conversion code
* model: loading hparams and tensors in k2-horizon.cpp
* model: K2 Horizon compute graph
* model: K2 Horizon compute graph adjustment and registering tokenizers
* model: K2 Horizon chat template and accomodate safetensors naming
* unicode : add the K2-Horizon pre-tokenizer splitter
The K2-Horizon regex had no arm in unicode_regex_split_custom and fell through to the
general std::regex fallback, which fails two ways.
On MSVC std::regex rejects \p{...}, so no K2-Horizon GGUF loads on Windows at all:
llama-quantize, llama-imatrix and llama-perplexity all abort with
regex_error(error_escape) before a token is produced.
Where the fallback does compile it is still wrong. unicode_regex_split collapses each
codepoint to a single byte naming its Unicode category before matching, and U+200C/U+200D
are category Control, which has no entry in k_ucat_cpt, so both become the 0xD0 fallback
byte. The literal and alternatives in K2's regex can then never match and
every ZWNJ or ZWJ ends a letter run.
The splitter is the existing llama3 one with a single rule widened, since K2's regex
differs from llama3's only in that a letter run also takes marks, ZWNJ and ZWJ.
tests/test-unicode.cpp gains a case for this: it fails before the change with
[Amy] [ZWNJ khaham] and passes after with the run intact.
* tests: expand K2 Horizon unicode splitter coverage
* unicode: handle K2 Horizon case folding and empty input
Assisted-by: Codex
* jinja : support sequence indices in selectattr and rejectattr
Assisted-by: Codex
* model : add K2 Horizon dense and MoVA support
Includes the K2 Horizon implementation from ifm-ai/llama.cpp with converter, tensor-parallel and model save/reload fixes.
Assisted-by: Codex
* chat : support K2 Horizon reasoning and tool calls
Assisted-by: Codex
* conversion: remove obsolete K2 Aurora alias
Assisted-by: Codex
* k2-horizon: enforce response schemas and load YaRN betas
Constrain final JSON after reasoning, accept flexible JSON tool envelopes,
enforce XML dialects, and handle repeated or alternate thinking markers.
Load YaRN beta metadata instead of retaining the default values.
Add schema, streaming, continuation, and model reload regressions. Validate
CUDA and CPU builds and 0.9B, 4B, and MoVA conversation/tool round trips.
Assisted-by: Codex
* renaming template fixture
* adressing cisc follows ups
* desloppify the parser / adress aldehir comments
* clean test-chat
* remove fallback : model trained mostly on high anyway
* fix k2 attn_v_exp tn splitting and metal fusion baseline
* k2-horizon : forward expand views before sums
* k2-horizon: copy embds before group norm to fix TP
* disable tesnor parallelism
---------
Co-authored-by: Ryandito Diandaru <ryandito.diandaru@mbzuai.ac.ae>
Co-authored-by: WestWaters <mario.papaleo2013@gmail.com>
Co-authored-by: Natani L. Mayday <71436458+TaskPuppyNatani@users.noreply.github.com>
Co-authored-by: West <100190545+WestWaters@users.noreply.github.com>
Co-authored-by: aaryamonvikram <aaryamonvikram@gmail.com>
Co-authored-by: aaryamonvikram <96529820+aaryamonvikram@users.noreply.github.com>
* server: support vision input for Clef
* move input_attn_causal to private
* extend old server_batch::embd
* server_batch::token::pos to multi dim
* nits
* fix abort
* fix img tokens cap
* fix yield_to_queue mutate data
* init conversion
* convert: ok
* model loaded
* add server code
* improve conversion script
* support shared prompt prefix
* add docs, imorove UX a bit
* add vision support
* add openjev tiny model for testing
* add dev docs
* support lev & kev
* clean up
* fix lev noul
* fix py lint
* nits docs
* clarify about not supporting date_facts
* convert : write Gemma embedding scale for DFlash drafts
A DFlash draft shares the target's token embeddings. Gemma scales them by sqrt(hidden_size) in the forward pass, and the draft config does not state that scale, so the converted draft read unscaled embeddings.
Take the scale from the target config when the draft config has none.
Assisted-by: Claude
* convert : check with get_model_architecture for gemma models
* vocab : honor BOS/EOS settings for PLaMo-2 and PLaMo-3
The original tokenizer configs for PLaMo-2 and PLaMo-3 have
`add_bos_token: true` and `add_eos_token: false` , but
_set_vocab_plamo() did not write the BOS/EOS metadata. The
PLAMO2 tokenizer path also ignored add_bos/add_eos during
tokenization.
Write the settings from tokenizer_config.json and honor them in
the PLAMO2 tokenization path. GGUFs without these keys keep the
previous behavior.
* Update conversion/base.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* convert: fix LoRA conversion crash for Qwen3.5 V-head reorder
_reorder_v_heads does reshape+permute+reshape to reorder V heads from
grouped to tiled order. LoraTorchTensor.reshape() cannot split its
row dimension (A matrix), so converting Qwen3.5 LoRA adapters that
target out_proj crashes with NotImplementedError.
Fix: detect LoRA tensors and apply the equivalent index permutation
directly — column reorder (dim=last) permutes A's columns, row
reorder (dim=0) permutes B's rows. This is mathematically identical:
(B @ A)[:, perm] == B @ A[:, perm]
(B @ A)[perm, :] == B[perm, :] @ A
Verified: both paths produce exactly zero diff against the full-tensor
reorder on random (rank=32, 4096×4096) matrices.
Fixes#21125
Signed-off-by: Radu Swigler <radu@swigler.com>
* convert: add ty: ignore for hasattr-guarded LoRA call
Assisted-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix comment
* nowrap
---------
Signed-off-by: Radu Swigler <radu@swigler.com>
Co-authored-by: Radu Swigler <radu@swigler.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Assisted-by: Claude Opus 4.6 <noreply@anthropic.com>
* Rebase GLM-Next support onto master, and migrate to llama-memory-hybrid-idx
* Add initial MTP support
* Merge branch optimizations. Reduce allocated compute buffer size, speed up long context decode, fla, and slight MTP improvements.
* Review driven changes, remove env vars, protect tensors
* Strip MTP for initial PR
* Clean up after mtp strip
* Clean up after mtp strip
* Update speculative.cpp
* Update llama-context.h
* Clean up after mtp strip
* Fix tokenizer ignore merges
* Improve quantization protection selection
* Refactor mhc helpers, graph base
* Lint Fixes
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Skip glm5-next in model saver, fix CRLF
* Skip glm5-next in sweep
* Remove T4 fallback
* Review cleanup
* Review suggestions
* Defer separate MTP gguf handling to MTP PR, drop filter
* Repad n_head_kv
* kpool init apply
* Order by descending score
* Drop guard
* read kpool from hparams, clarify kpool cache flags, remove kpool_build_state(nullptr)
* Add glm5-next support to model saver and add arch test fixture
* Review cleanup
* Kpool pooled caching clarify
* Add multi stream support
* Finish Rebase
* Sparse FA fir DSA prefill
* Const
* Update llama-model.cpp to fix rebase error
* gguf-py : merge tensor map entries for HC tensors
* model : use build_gdn_l2_norm in GLM5_NEXT implementation
* chore : remove trailing whitespace
* model : use new OP precision setting API in GLM5_NEXT implementation
* mtmd : use ggml_swiglu_clamp in GLM5V and apply the image token limit
The two clamps around swiglu_split are what ggml_swiglu_clamp already does,
so the clamp bounds collapse back to one value. GLM5V also never called
set_limit_image_tokens(), so --image-max-tokens had no effect.
Assisted-by: Claude Opus 5
(cherry picked from commit 46d18e12d422be4cc04a70e4a9a9e0168bb3d5b7)
* llama : keep the GLM5-Next k-pool layout across ubatches
The layout was rebuilt from a full cell scan on every ubatch. Pools are fixed
by the positions relative to the sequence's first one, so the layout now lives
on the memory and a ubatch only appends to it.
A sequence edit no longer stales every pooled key either, only the ones at or
after the edited position, which makes a tail seq_rm free. The pooling subgraph
is built unconditionally so the graph shape no longer changes every kpool
tokens, and the pool axis is folded into rows before soft_max, which otherwise
exceeds the CUDA gridDim.y limit past n_kv 262144.
Assisted-by: Claude Opus 5
(cherry picked from commit 5d1c40b93e17fddbf73b785efe43e0d02ccb3977)
* model : write the GLM5-Next recurrent rollback checkpoints
The conv state and the delta net state were only written to the live row, so a
rollback restored whatever the checkpoint rows happened to hold. Take the same
route as kimi-k3: build_recurrent_attn for the state, and write all K_rs conv
groups. That also drops a state view that assumed contiguous rows.
Enroll the arch in test-recurrent-state-rollback, which catches this under its
garbage-filled cache pass.
Assisted-by: Claude Opus 5
(cherry picked from commit 5ace37e86d5d448e83ef5dde5632c748185b18cd)
* llama: fix PR #27773 test-save-load-state restore failure
Clear the attention and indexer cache data after a failed hybrid state restore so restored NaNs cannot affect a later sequence.
Assisted-by: Codex
* llama: fix PR #27773 gpu-rocm graph reallocation
Reserve the full GLM5-Next pool capacity and dirty pool count. The gpu-rocm Test step aborts when n_new grows while the graph node count stays fixed; CUDA, Vulkan, Metal, and WebGPU checks report the same error.
Assisted-by: Codex
* llama : fix GLM5-Next k-pool layout staleness after edits and shared teardown
Two defects in the cross-ubatch k-pool layout added by the k-pool commit:
1. Wrong results. An edited sequence only rebuilt its pool layout when its cell
count changed, so if the first ubatch after an edit added back exactly as many
cells as were removed, the stale position-to-cell list survived. With a unified
cache and more than one sequence, where another sequence takes the freed cells,
the reused layout points at the wrong cells (CPU: large logit drift, CUDA: NaN).
Rebuild whenever the sequence is stale, not only on a size mismatch.
2. Slowdown. "shared" mode was assumed to end only with an edit that forces a
rebuild, but sharing also ends when the other sequence is removed. The survivor
kept shared = true, pinning cache_safe off and re-pooling every pool on every
ubatch (server trigger: n>1 completions with -kvu, via the seq_cp in
copy_state_to). In seq_rm, if the layout has shared cells, stale every sequence
so one rebuild re-derives sharing and cache_safe returns to 1.
Assisted-by: Claude Opus 5
* llama : fix build_attn_mha stream stride for non-contiguous q
build_attn_mha split the batch into streams with a stream stride of
q->nb[3]/n_stream. That only equals one stream's span, (ne[2]/n_stream)*nb[2],
when q is contiguous. GLM5-Next is nope-only, so it does not concat a rope part
and passes the permuted q_absorbed straight in, where nb[3] != ne[2]*nb[2]; the
stride was then n_head times too large and every stream s >= 1 read another
head's queries. Split-KV (-np N without --kv-unified) multi-stream prefill was
wrong for every stream past the first. Unified KV and decode were unaffected
(n_stream == 1, and decode takes the gather path). Other MLA models concat rope
so q is contiguous and the computed value is unchanged for them.
Compute the stride from the token dimension, which is identical for a
contiguous q.
Assisted-by: Claude Opus 5
* llama : re-derive GLM5-Next k-pool sharing on state_read/state_drop
The shared-cell teardown added to seq_rm (stale every sequence when the layout
has shared cells, so a survivor does not keep shared = true and pin cache_safe
off) was missing from the other paths that can free shared cells: state_read
and state_drop staled only the one sequence. Apply the same re-derivation there
and correct the comment that claimed sharing ends only via an edit or seq_rm.
Assisted-by: Claude Opus 5
* quant : drop duplicate GLM5-Next hc_ filter
The hc_ name filter was listed twice in the GLM5_NEXT protection block.
Assisted-by: Claude Opus 5
* glm5-next: scope K-pool cache access to indexed operations
* glm5-next: keep K-pool access in hybrid index memory
* glm5-next: keep mHC graph builders model-local
* glm5-next: mark only touched pools per ubatch
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
* model : support classifier_pooling for ModernBERT rerankers
Assisted-by: Claude Opus 5.5
* model : read classifier pooling type in load_hparams
Write classifier.pooling_type from _try_set_pooling_type whenever the
config has classifier_pooling, and read it in
llama_model_base::load_hparams. ModernBERT falls back to mean when it
is unspecified.
Assisted-by: Claude Opus 5.5
* conversion : only accept cls and mean for classifier_pooling
Assisted-by: Claude Opus 5.5
* model : rename classifier_pooling_type to pooling_type_cls
Assisted-by: Claude Opus 5.5
Recent PLaMo-3 models use YaRN, while some earlier PLaMo-3 models do not.
The recent PLaMo-3 store their YaRN settings as flat config keys
(rope_scaling_factor, initial_context_length) and build the dict at runtime
in Plamo3Config.rope_parameters. The current converter misses these settings
and writes plain RoPE metadata to GGUF. Mirror the runtime settings into
rope_parameters so the corresponding rope.scaling.* is written to GGUF.
fzowlandSigbjørn Skjæret
Xue ZhenliangandSigbjørn Skjæret
Gabe Goodhart
Daniel Han
aiqubits
Mikhail Podvitskii
FINAL-Bench
Xuan-Son Nguyen
Nigel JonesandGabe Goodhart
Kartik Gulia
Takamichi Ohara
bri-prismandGeorgi Gerganov
ynankani
Hrishith Thadicherla
bosh
Sam Malayek
Tarek Dakhran
Terrence Zhao
SIDDARTHA REDDY
Toki Nasin
Yu Chengye
Aman GuptaandGeorgi Gerganov
