Sync master with upstream release b10453 - #623
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* Switch ROCm from 7.2.1 to 7.14 ROCm 7.14 is the first production release using TheRock build system. It can be installed using multi-arch deliverables from wheels, debs, rpms, tarballs or runfiles. Adjust ROCm targets for Linux and Windows to use this instead. * ci: switch all other Windows ROCm jobs to ROCm 7.14 wheels Move the shared windows-setup-rocm composite action from the HIP SDK PRO Edition installer to the multi-arch ROCm wheels (rocm[libraries,devel]). The wheel-install logic that previously lived inline in release.yml is now in the shared action, and both build-cache.yml and release.yml call it. Also migrate the build-cuda-windows.yml hip job to the same wheel-based layout (cache path/key, rocm-sdk environment setup, llvm/bin compiler paths) so it keeps working after the action's contract changed; drop its now-unused ROCm 7.2.1 rocWMMA download and stale include path.
…ml-org#26566) * test new flash_attn test * rebase and fix to disable subgrou matrices when max_kv_tile == 0 * delete log output * Add i32 support to cpy and enables the all ops test * restore the non target ci tests * comment out of TODO of build-cpu.yml * fix format
* model : fix SWA not being enabled for EXAONE 4.5 load_arch_hparams tests `hparams.n_layer() == 64` before LLM_KV_NEXTN_PREDICT_LAYERS has been read. n_layer() returns n_layer_all - n_layer_nextn and n_layer_nextn defaults to 0, so a GGUF carrying the MTP head (block_count=65, nextn=1) evaluates to 65 and the whole SWA block is skipped. The model type switch further down in the same function reads 64, because by then the key has been loaded. n_swa is still filled in by the unconditional get_key below the block, so llama_model_n_swa() reports 4096 and the logs look correct while only swa_type stays LLAMA_SWA_TYPE_NONE. This affects the official LGAI-EXAONE GGUF release as well. EXAONE 4.0 has no MTP head, so block_count is 64 there and the check matches. * model-loader : skip TENSOR_SKIP tensors in the metadata-only path create_tensor asserts on a null buffer type when building from metadata alone, but buft_for_tensor returns null by design for tensors marked TENSOR_SKIP, which is how architectures with nextn/MTP layers mark theirs. Those models cannot be constructed by llama_model_init_from_user at all. The file-backed path below already returns nullptr for the same tensors, so callers see the same thing either way. * tests : cover exaone4 hparams ordering Builds a synthetic exaone4 model with the layout the shipped EXAONE 4.5 GGUFs use (block_count 65 + nextn 1). The swa_type check is the one that catches the ordering bug; the n_layer_nextn and n_layer() checks only tell a broken fixture apart from a real regression. Fails before the ordering fix with "swa_type is not STANDARD", passes after. * Revert "tests : cover exaone4 hparams ordering" This reverts commit d2f3baf. * Revert "model-loader : skip TENSOR_SKIP tensors in the metadata-only path" This reverts commit aecb9bc.
* test-backend-sampler: skip multi_output_sampling_chain on HIP The new multi_output_sampling_chain test uses top_k, whose backend probs path needs CUB (unavailable on HIP), so sampled_probs is null and the test aborts. Add it to the existing HIP skip list alongside the other TOP_K tests. * ci: keep gpu-rocm logs in a per-run dir keyed by GitHub run id The self-hosted gpu-rocm runner can't upload logs to Azure blob (egress firewalled), so a run's logs were wiped by the next run. Write each run's logs to $OUT/run-<run_id>-<attempt>/ so an Actions run URL maps to its logs. * test-backend-sampler: also skip multi_output_cpu on HIP Like the other TOP_K-based subtests, multi_output_cpu's backend sampler never initializes on HIP (no CUB TOP_K), so it aborts. Add it to the skip list. --------- Co-authored-by: Jim Wu <ywu@xilinx.com>
* tests : remove fetch_server_test_models.py * ci : use tests.sh wrapper of pytest
…rg#26081) * llama: add new default load-mode auto which picks mmap unless a non-Metal iGPU is used * Update ggml/src/ggml-hexagon/ggml-hexagon.cpp Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com> * set mmap_support to false on OpenCL backend * fix order of load modes * use -1 for auto * resolve load mode auto earlier to correctly pick gpu host or cpu memory * add load mode auto to llama-bench * bump virtgpu api version, regenerate docs --------- Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com> Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
ggml-org#26890) This commit updates the python script that runs the original model to generate embeddings for the causal model, to use save_output_data which stores the token ids and the prompt in addition to logits. The motivation for this is that the embedding logits verification will fail as it expects these files (-prompt.txt and -tokens.bin) to exist. With the changes in this commit the causal-verify-embeddings target works again.
Most of the old ones have been resolved (yay) but the recent refactor of mmq paramters has caused some symbol names to change, leaving a couple of non-ignored failures
* webui: hide loaded model in context gauge at single-model mode * webui: keep context gauge details open state across reopens
* adapt the api * text model ok * working impl, need verify and clean up * mtmd: build the pocket-tts transposed convolutions as GEMM + col2im ggml_conv_transpose_1d has no grouped mode, so the depthwise upsample was built as one convolution and one concat per channel, which floods the graph with small nodes and makes kernel launches dominate the decoder. Fold both cases into the column form the seanet decoder already needs: the general case reshapes the kernel to [IC, K * OC] and matmuls it with the input, the depthwise case batches a matmul over the channels so a step scales its own kernel. A single col2im_1d then scatter-adds the columns back to the signal, with the same shape as before, so the overlap-add tail, the streaming state and the bias are untouched. Generation time per frame drops by 80% on CUDA and by 50% on CPU. The output matches the previous implementation sample for sample, with a correlation of 0.999994 and identical frame counts. * flow_temp + frames_after_eos * chunking * mtmd: carry the remaining pocket-tts per-pack settings The language packs also tune the end-of-speech padding and the padding of short prompts, next to the temperature already carried in the mmproj: french_24l asks for 8 tail frames instead of the guessed 3, english_2026-01 asks for short prompts to be padded with spaces. Write both in the mmproj as clip.gen.audio.frames_after_eos and clip.gen.audio.pad_short_text, keyed on the pack in the conversion script like the temperature. The loader keeps them optional, so a mmproj without them behaves as before. Map semicolons to commas for every pack instead, the reference only asks for it on three of them and it costs nothing elsewhere. Existing mmproj files must be converted again to carry the two keys. On a long french text the port now lands within 2% of the reference: 22.96s against 23.44s, with the same peak level and the same amount of silence. * clip.gen.audio.model_variant * clean up code comments * nit: drop the dead flow_temp hparam, the pack table holds the default * update docs * address security problems * less invasive base.py * lint * add mtmd_gen_inp_default * add docs * rm gen_flow_temp --------- Co-authored-by: Pascal <admin@serveurperso.com>
* conversion: skip untrained DFlash embeddings * Add Nemotron DFlash support * Add DFlash NVFP4 support * Address review comments * add missing output_s for nvfp4 * Include change for keeping residual for last layer also if requested in future dflash models * Update conversion/qwen.py Defensive check, not needed Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Fixing bug introduced by merge conflict --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
…g#26903) Signed-off-by: ynankani <ynankani@nvidia.com>
* spec : update speculative-simple * cont : simplify * cont : clean-up
…6879) * chat : fix muse-glimmer swallowing a trailing tool call into content Muse Glimmer routinely answers the user and calls a tool in a single generation. The template terminates a message with <|eom|> when more messages follow in the same turn and <|eot|> only at the end of the turn, so the answer is closed by <|eom|> and the call opens a fresh header: <prose><|eom|><|start|>assistant to=<tool><|message|><atem:function_calls>... The final-message rule read content with until("<|eot|>"), which assumed the user-facing message is always last. There is no <|eot|> before the call, so content ran to the end of the turn, absorbed the markup, and no tool_calls were emitted - the tool never ran. On a tau2-bench telecom run this hit 43 turns across 19 of 114 tasks. Stop the answer at <|eom|> and parse what follows as tool calls. Adds models/templates/muse-glimmer.jinja and four parser tests: a plain answer, the <|eom|> junction, markup quoted in an answer staying content, and tool markup inside the to=self channel staying reasoning. * address comment
…-org#26882) * fix: handle nested global_head_dim in Gemma4 config Gemma-4 E4B models have global_head_dim inside text_config rather than at the top level. Add fallback to support both layouts. * fix: add fallback for global_head_dim to support per_layer_config format * fix: read head_dim only from full_attention layers in per_layer_config and num_global_key_value_heads compatibility * fix: added fallback for num_global_key_value_heads * fix: read per_layer_config from root hparams * fix: delete unused text_config * cleanup and fixes --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
…all (ggml-org#26892) * wavtokenizer-dec : bound posnet/convnext block_count against n_layer_all * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* vulkan: TQ2_0 (ternary) support — dequant + dedicated mul_mat_vec + matmul via dequant_funcs First Vulkan ternary type in ggml. Correctness: OM-125m TQ2_0 vs F16 top-12 logprobs identical to 4 decimals fully offloaded (float dequant path, no Q8_K activation quant). Speed at 125m ~= F16 (overhead-bound at this scale); the bandwidth win targets larger BitNet SKUs. MMQ/int-dot path intentionally not wired yet. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * tests: enable TQ2_0 in backend-ops type lists Vulkan now implements TQ2_0 (dequant, mul_mat_vec, mul_mm, get_rows); backends without support skip via not-supported as usual. TQ1_0 stays disabled. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> --------- Co-authored-by: Michael Trabalka <michael.trabalka@sqv.ai> Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
This commit tries to make the logging of target_layer_ids a bit clearer and easier to read. Currently the output generated looks like this: ```console 0.00.468.624 D load_arch_hparams: DFlash extract_layers = [0.00.468.626 D 2, 0.00.468.626 D 6, 0.00.468.626 D 20, 0.00.468.626 D 30, 0.00.468.627 D 42, 0.00.468.627 D 520.00.468.627 D ] ``` With the changes in the commit the output will be: ```console 0.00.522.765 D load_arch_hparams: DFlash extract_layers = [2, 6, 20, 30, 42, 52] ```
Port of ggml-org#23940. Arc Pro B70, Qwen 3.6 27B Q4_K - Medium (48 of its 64 blocks run gated_delta_net), -ngl 99 -fa 1 -ctk f16 -ctv f16 -b 2048 -ub 2048, interleaved A/B passes of r=3: tg128 23.91 / 23.90 / 23.90 -> 24.19 / 24.17 / 24.20 +1.2% tg128 (rebuild) 23.81 / 23.81 -> 24.09 / 24.10 +1.2% pp2048 1050.8 / 1053.9 -> 1053.8 / 1054.5 flat 2 seqs, tg128 32.73 / 32.75 -> 33.11 / 33.10 +1.1%
…-org#26779) Measured on Arc Pro B70 (Battlemage, Level Zero), llama-bench -r 20, two interleaved rounds, tg128: qwen2.5-3B-Instruct Q4_K_M 154.18 -> 158.53 t/s +2.8% gemma-2-2b-it Q4_K_M 162.45 -> 165.62 t/s +2.0% llama-batched-bench on qwen2.5-3B, S_TG by batch size: B=1 142.72 -> 147.57 t/s +3.4% B=2 243.72 -> 268.26 t/s +10.1% B=4 359.58 -> 398.02 t/s +10.7% B=8 449.75 -> 505.63 t/s +12.4%
…rs (ggml-org#27043) Scrub developer-specific /home/<user>/ paths from example docs and test fixtures so they don't leak into the tree. - examples/test-cmake/README.md: /home/danbev/... -> /path/to/llama.cpp/... - tests/test-chat.cpp: /home/jarvis/... -> /home/user/... (input and expected string kept identical so the parser test still passes) Co-authored-by: Jim Wu <ywu@xilinx.com>
…ml-org#27041) * server_queue::worker * call llama_decode inside yield_to_queue * also handle process_mtmd_chunk * clean up * nits * rm test
* llama : allow virtual igpu devices * cont : better comment
* Initial changes for Recurrent state rollback for nemotron for cpu and cuda * Removing CPU RS rollback. Will enable it in subsequent PRs * addition of test case * Removing assert and calling runtime API to check if op is supported * removing extra API and updating the call sites for K * replace static cuda detection to runtime fused_op api * address review comments and fallback when SSM rollback not supprted * Adding changes for supporting RS-rollback in CPU. Also added test-backend-ops for cpu and cuda * removing memory manipulation as rs rollback is now supported in CPU * removing the static probe which is not needed now * correcting the format * address review comments * enabling test for all the backends, unsupported backends will fallback to CPU * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * choose different graph based on the result of fused_ssm_op is supported or not and also handled memory->n_rs_seq >1 case incase of op is not supported * Support K > 1 in ssm_scan for all backends * Fix CI Issues --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Gaurav Garg <gaugarg@nvidia.com>
* chat: add reasoning_effort to common_chat_templates_inputs Store OpenAI Chat Completions reasoning_effort and make it available to jinja templates (with model specific translations where required). Assisted-by: llama.cpp:Muse-Glimmer-30B * server : fixup reading reasoning effort from body server_chat_convert_responses_to_chatcmpl already handles conversion of Responses API reasoning.effort to reasoning_effort * chat : expose reasoning effort Assisted-by: Claude Opus 5 * chat : add reasoning_effort to generation_params Assisted-by: Claude Opus 5 * chat : move reasoning_effort next to enable_thinking Assisted-by: Claude Opus 5 * cont : mirror preserve_reasoning * cont : pass context through analyze function --------- Co-authored-by: Alde Rojas <hello@alde.dev>
* jinja : fix quadratic cost in gather_string_parts * fix some comments * remove test
* apply fixes * cont * revert gguf fix
…alLM (ggml-org#27018) * llama : support for MiniMax-Text-01 model * chore : renames to match the other MiniMax models * model : add logits mask as MiniMax-Text-01 embeddings tensor has zero-valued embeddings for tokens >= 200032 that produce zero logits disrupting the token sampling process * llama : replace hardcoded conditions with hparams.is_recr() * model : used build_rs() for recurrent state management * chore : code cleanup * model : optimized MiniMax-Text-01 by removing the state tranpose operations * chore : removed unnecessary ggml_cont() in MiniMax-Text-01 implementation * llama : add generic logits mask graph input * model : permuted diag_decay dimensions to avoid doing it inside MiniMax-Text-01 graph * chore : code cleanup * chore : code cleanup * model : use token positions when calculating MiniMax-Text-01 decay tensors * convert : add support for MiniMaxM1ForCausalLM as it seems to be the same as MiniMaxText01ForCausalLM * chat : add jinja template for MiniMax-M1 Co-authored-by: QscQ <qscqesze@gmail.com> * chore : code cleanup * tests : MINIMAX_01-related fixes * chore : silence Python lint errors * vocab : remove unnecessary vocab type * convert : update MiniMaxText01Model conversion to use yield when modifying tensors * convert : suppress tokens with zero-valued embeddings during MiniMax-Text-01 conversion * llama : removed logits mask - no longer necessary as token suppression is used instead * model : use common functions to make MiniMax-Text-01 implementation more concise Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * model : use common functions to make MiniMax-Text-01 implementation more concise Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * convert : override non-working built-in chat template during conversion * tests : skip arch MINIMAX_01 tests for WebGPU backend (it breaks again) --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com> Co-authored-by: QscQ <qscqesze@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* mtmd: fix granite 4v grid assembly (cherry picked from commit 91f82eb) * mtmd: fix truncation for scaled image height and width before unpad Signed-off-by: Hemanth Battu <hbattu@ibm.com> * mtmd: remove MTMD_DUMP_EMBD debug scaffolding Signed-off-by: Hemanth Battu <hbattu@ibm.com> * clean up comments, clarify about anyres_info excluded from serialization * add_newline is now dead code --------- Signed-off-by: Hemanth Battu <hbattu@ibm.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: Hemanth Battu <hbattu@ibm.com>
…org#26934) Replace the deprecated --mmap, --no-mmap, --mlock, and --direct-io flags with the unified --load-mode argument across scripts, examples, and documentation. Internal warning message and env var docs updated accordingly. Signed-off-by: Fathi Boudra <fathi.boudra@linaro.org>
…l Xe (ggml-org#25380) * vulkan: add SHMEM_STRIDE_PAD/APPLY_SLM_A_RESHAPE for coopmat mul_mm on Intel Xe * vulkan: fix shmem estimate for Intel SHMEM_STRIDE_PAD=0 in matmul_shmem_support * cacheline aligned for shared kvalues_mxfp4 * vulkan: fix OOB read in kvalues_mxfp4 init after cacheline padding * vulkan: restrict SLM-A reshape to Intel Windows driver, revert mxfp4 cacheline padding
* fix: check gguf array type before reading * update skill
…4431) * common: support --models-dir loading MTP assistant models * common: preset: check for MTP models with strict prefix * common: preset: Take advantage of PR ggml-org#27005 * handle other draft types * drop eagle3 * clean up --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* run common_speculative_process in worker * swap worker <--> main thread design
* model: add Kimi-K3 text model
Hybrid KDA (linear) + MLA (full) attention as in Kimi-Linear-48B, plus five
things that architecture does not have:
1. cross-layer residual attention (attn_res_block_size)
2. latent MoE (routed experts run at n_expert_latent)
3. situ activation (replaces SwiGLU everywhere)
4. MLA output gate (sigmoid gate before o_proj)
5. full-rank KDA gate (single ssm_g instead of ssm_g_a/ssm_g_b)
K3's text_config reports KimiLinearForCausalLM - the older 48B architecture -
so get_model_architecture routes on the top-level name instead.
The KDA decay gate has two forms, selected by linear_attn_config's
gate_lower_bound. It is not a clamp: when set it swaps the activation entirely
(fla/ops/kda/gate.py), from -exp(A_log)*softplus(x) to
lower_bound*sigmoid(exp(A_log)*x). K3 sets it to -5.0; kimi-linear leaves it
unset, so that path is unchanged.
Cross-layer residuals reuse ggml_dsv4_hc_pre for the weighted sum. That op is
CPU + CUDA only, so Metal/Vulkan will fall back per-node until those kernels
exist.
The routed experts ship as compressed-tensors "mxfp4-pack-quantized". That is
bit-compatible with ggml's MXFP4 - same E2M1 code assignment, same E8M0 scale
byte, only the nibble positions within a block differ - so they are repacked
rather than dequantized, losslessly and without a ~5.5 TB bf16 round-trip.
The repack is built lazily because gguf_writer holds every added tensor until
the final write. DeepSeek-V4 was already doing the identical bit-shuffling, so
it now shares the helper.
Verified against Moonshot's own code path (transformers + fla's Triton KDA
kernels) on a tiny model exercising every K3-specific feature. Final-position
logits vs the fp32 reference: 6.7e-05 rel / corr 1.00000000 for both the
chunked and the recurrent delta-net path. MXFP4 blocks dequantize to the source
weights with 0.0e+00 error.
Assisted-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* model: fix ty errors in the Kimi-K3 converter
- `_res_parts` buffers (kind, tensor) pairs, not bare tensors
- `get_tensors` must return an Iterator, matching ModelBase
- LazyBase's `func` takes one argument, so pass the expert loaders through
`args` instead of the closure
- borrowing KimiLinearModel.set_vocab from an unrelated TextModel is
deliberate and safe, but not expressible in the signature
No behaviour change: the MXFP4 repack still dequantizes to the source weights
with 0.0e+00 error and end-to-end logits are unchanged (8.386e-03 rel,
corr 0.99996630).
Assisted-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
* Update conversion/kimi_k3.py
Co-authored-by: Boris Dvorkin <b_dvorkin@niuitmo.ru>
* Increase LLAMA_MAX_EXPERTS from 512 to 1024
* tests : support for Kimi K3 in archs test
* chat : add Kimi K3 chat format (reasoning, content, typed tool calls)
K3's assistant output is an XTML-ish tagged format built by the template's
open_tag/close_tag macros. Two properties break generic parsing:
1. The generation prompt ends with open_tag('think'), so the completion
starts inside the think section with no opening marker in the output
(thinking_forced_open).
2. Only <|open|>/<|close|>/<|sep|>/<|end_of_msg|> are special tokens; tag
names ("think", "response", "message") are ordinary text tokens.
Adds common_chat_params_init_kimi_k3 (PEG_NATIVE) with detection on the
marker trio, reasoning extraction, response unwrapping, and tool-call
parsing of the tools/call/argument tag structure with argument types
taken from the tool schema. Includes the K3 chat template fixture and 9
test-chat cases derived from real generations of the full 2.8T model.
Verified end-to-end against Kimi-K3-Q2_K (GrEarl/Kimi-K3-GGUF) on 8x B200:
content, reasoning_content, streaming deltas, and tool_calls all correct;
finish_reason stop/tool_calls as appropriate.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* chat : add message_delimiters for Kimi K3
Per-role message-start markers for token-level span splitting. User and
assistant messages carry only the role attribute, so their full opener
(through <|sep|>) is used; system and tool messages continue with more
attributes (type=/tool=/index=), so those delimiters stop after the
role's closing quote. Verified against the K3 tiktoken vocabulary that
the closing quote is always a standalone token across all attribute
variants, so the token-level prefix match stays exact.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix: apply nits from @ngxson and text fixes from @danielhanchen
* tests : added missing hyperparameters and tensors for Kimi K3 in test-llama-archs
* chore : move overly verbose header file comments to Kimi K3 source file
* tests : re-enabled KIMI_K3 in test-llama-archs for WebGPU backend
* model-saver : emit kda_gate_lower_bound for Kimi K3
Quick fix. The Kimi K3 loader reads kda_gate_lower_bound and gates a graph branch on it (it scales the KDA gate when the bound is above -INFINITY), but the model
saver never wrote the key, so a save->load roundtrip silently dropped it back to the -INFINITY default and changed the model's output. The real K3 config sets gate_lower_bound = -5.0.
I propose to emit it from the saver, and set it to -5.0 in the test-llama-archs K3 case so the roundtrip check exercises it (the roundtrip fails without the saver line).
* Refactor conditional for model architecture check
* tests : re-enabled (again) KIMI_K3 and MINIMAX_M3 in test-llama-archs for WebGPU backend
* fix code comments
* add template on conversion
* move repack_mxfp4_blocks to model base
* nits
* add_value_length
* optimize res_stack construction
* nits
---------
Co-authored-by: Boris Dvorkin <b_dvorkin@niuitmo.ru>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Deepankar Singh <singh.deepankar39@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Caleb DeLeeuw <caleb.deleeuw@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
ggml-org#26562) * ui: mask API Key field in settings and error splash to stop browser autofill * ui: set autocomplete=new-password on private fields The password input type makes browsers offer to save the API key in the password manager and autofill saved site credentials into the field. The new-password autocomplete value disables both. --------- Co-authored-by: Pascal <admin@serveurperso.com>
* llama : check LoRA tensor data is within file bounds * Update src/llama-adapter.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
…ent (ggml-org#27130) * better supports_string_content cap detect * test: add "skip" * messages_inp_normalizer
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
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Updates dev branch with latest release (b10453) from ggml-org/llama.cpp