* WIP OCP FP8 E4M3 support
* Fix python type-checker
* Materialize input_scales also for FP8 when converting
* Fix conversion of https://huggingface.co/RedHatAI/Qwen3.8-27B-NVFP4
1. Per row FP8 wrongly fell to NVFP4 conversion, as NVFP4 checked on
dims alone. Also check on dtype for FP8
2. Need to reshape FP8 QKV projections scales in the same way that
weights are reshaped
* Allow remaining FP8 tensors to be dequantized during conversion
* Allow tensor-strategy for FP8-only compressed-tensor ckpts
* Fix and test gguf-py behavior for FP8_E4M3
* Disallow QKV-fusion for FP8 temporarily
* Fix conversion of fused qkvz & FP8 for Qwen3-Next
* Avoid fusing FP8 for --fuse-gate-up-exps as we may have different ws
* Emit warnings when not fusing FP8 during conversion
* More FP8 conversion fixes
* Add supported for strided copies to/from FP8 in CPU
* remove unneeded scratch buffer from FP8 cpu cpy/dup path
* add explanatory comments and TODOs
* Add dedicated encoder and decoder tests for FP8
* Whitelist ggml_op_get_rows in metal instead of blacklisting
required to fix failing CI, and white-listing should be more
future-proof than adding FP8 to the black-list
https://github.com/ggml-org/llama.cpp/actions/runs/38433916498/job/115422880633?pr=27512
* Fix more conversion bugs
* Defer weight materialization during conversion
* Blacklist fp8 in metal backend instead of white-list
* Adjust indents
* Fix lint + execute tests
* Add fast CPU kernel for F8_E4M3
* Conversion fixes
* Fix remaining gaps
* Enable tied weights with shared scales
* Make mandatory-F32 rules apply to FP8 during conversion
* Fix python type checks
https://github.com/ggml-org/llama.cpp/actions/runs/39765670052/job/119780379595?pr=27512
* Fix kaleidai unit-test failures
https://github.com/ggml-org/llama.cpp/actions/runs/39765670125/job/119780383150
* Relax numeric precision constraints for pre-check
* Leave fast-FP8 CPU on RISC-V for follow-up
We already exclude arm from the fast-path
* Allow output scale tensors to be located in any gguf shard
* Move test_conversion_fp8.py test execution to behavior-check.yaml
* Apply batched suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Move mixed compressed-tensor logic into dequant_fp8 + add unit-tests for it
* Install all requirements also on Windows for behavior-check
Fixes https://github.com/ggml-org/llama.cpp/actions/runs/40535852590/job/122300758223?pr=27512
* Unmap lazy tensors before clean-up in Windows
Should fix https://github.com/ggml-org/llama.cpp/actions/runs/40539797824/job/122314782459?pr=27512
* LF line endings
https://github.com/ggml-org/llama.cpp/actions/runs/40539798168/job/122314791118?pr=27512
* Extract FP8 tests from llama-arch to its own test-fixture
See https://github.com/ggml-org/llama.cpp/pull/27512#discussion_r4400628221
* Update gguf-py/gguf/scripts/gguf_convert_endian.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* kv-cache : use ggml_backend_tensor_copy to refill the DSV4 K mirrors
* llama : move the Engram pad id to llama_hparams and the token map to llama_model
The saver no longer needs the deepseek41 model class.
* deepseek41 : drop the F32 src1 request on the HC mixes matmul
* ggml : add softplus TODOs in the backends that still use log(1 + exp(x))
* convert : make the DSV4/DSV4.1 modify_tensors generators, drop the redundant vision filter
* deepseek41 : clean up comments
* deepseek41 : drop the F32 src1 requests on the router and indexer matmuls
* convert : use format_tensor_name directly in DeepseekV4Model
* s/comp_kv_src/comp_il_src/g
* cont : fix TODOs for log1p
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.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>
* convert : skip bias_vl tensor in DeepSeek-V4 DSpark conversion
The DFLASH arch does not include FFN_EXP_PROBS_B_VL, so the DSpark
conversion failed when it tried to write the mtmd-only hash routing
tensor ffn.gate.bias_vl. Drop it like the tid2eid tensor; the DFLASH
draft only consumes ffn.gate.bias via FFN_EXP_PROBS_B.
Assisted-by: pi:llama.cpp/DeepSeek-V4-Flash-0731
* cont : fix
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* 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>
* common/chat: update DeepSeek V4 templates
Align the DeepSeek V4 templates with the official encoders while keeping parser behavior out of this change.
- Default drop_thinking for DeepSeek V4 history so prior thinking is omitted unless preserve_reasoning is requested or tools are present.
- Add structured output response-format instructions to the V4 templates and pass the schema into template rendering.
- Add a separate Flash 0731 template for the updated high and max reasoning effort mapping.
- Cover reasoning effort, drop_thinking, structured output prompts, preserved reasoning, continuations, and empty tool arguments in template rendering tests.
Official references:
https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/blob/main/encoding/encoding_dsv4.pyhttps://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731/blob/main/encoding/encoding_dsv4.py
Assisted-by: Codex
* Fix deepseek v4 0731 template selection
* remove unneeded lower normalization
* Fix DSML parser to consume the tool call separator
* address aldehir requests
* address aldehir comment
* llama : MTP support for DeepSeek V3.2
* model : no need to include MTP layers during DeepSeek V3.2 model type discovery
---------
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
* convert: add dsv4 conversion
* add basic setup
* add llm_graph_input_dsv4
* add save-load state
* add sinkhorn eps - correction by @fairydreaming
* add rope fix
* cleanup dead code
* fix bugs
* support pro model: added by @fairydreaming
* remove redundant V cache
* Chat template
* remove debugging leftovers
* Add mechanism for inlining templates based on architecture
* s/deepseek-v4-flash/deepseek4/g
* s/deepseek-v4-flash/deepseek4/g continued
* enable graph reuse
* enable FA
* fix test llama archs
* rename
* compatibility with antirez ds4 GGUFs
* simplified set_gguf_parameters() by calling super class method, replaced moe.score_func with expert_gating_func.
* reserve worst-case kv-cache
* revert max split inputs
* address review comments
* add padding to enable FA
* pad only the final value of plan.n_kv to 256
* remove built-in cpp chat template
* cont: remove cpp built-in template
* rm outdated test
* replace ggml_view_3d() with ggml_reshape_3d()
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* only support n_seq=1 for now
* remove unused var
* cont: remove unused var
* use scale bias
* use correct ptr for can_reuse
* remove gen-chat-inline-templates.py
* simplify graph reuse
* cont: cleanup
* remove unused inputs
* enable partial checkpointing
* add correct shape for kq_mask + set llama_model_n_swa to 0 for dsv4
* precompute source_idx + add comment about dummy write
* support multi-seq
* remove restored_trim_pos
* use split_equal when possible
* fix indent
* address review comments
* use LLM_KV
* fix ci
---------
Co-authored-by: Piotr Wilkin <piotr.wilkin@syndatis.com>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: fairydreaming <166155368+fairydreaming@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* move conversion code to a dedicated conversion directory and split the files akin to the src/models architecture
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Oliver SimonsandSigbjørn Skjæret
Aman GuptaandGeorgi Gerganov
aiqubits
Xuan-Son Nguyen
Saba Fallah
Thiago Padilha
fairydreamingandStanisław Szymczyk
Saba FallahandXuan-Son Nguyen
