tqcq/llama.cpp
0
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-08-17 05:25:09 -04:00
Code Issues Packages Projects Releases Wiki Activity

model : add support for SmallThinker series (#14898)

Browse Source 
* support smallthinker

* support 20b softmax, 4b no sliding window

* new build_moe_ffn_from_probs, and can run 4b

* fix 4b rope bug

* fix python type check

* remove is_moe judge

* remove set_dense_start_swa_pattern function and modify set_swa_pattern function

* trim trailing whitespace

* remove get_vocab_base of SmallThinkerModel in convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* better whitespace

Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* use GGML_ASSERT for expert count validation

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Improve null pointer check for probs

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* use template parameter for SWA attention logic

* better whitespace

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* move the creation of inp_out_ids before the layer loop

* remove redundant judge for probs

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Dongliang Wei
2025-07-28 19:47:00 +08:00
committed by GitHub
parent afc0e89698
commit 6c6e397aff
10 changed files with 443 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

13
src/llama-hparams.h
View File

@@ -140,7 +140,7 @@ struct llama_hparams {
// for Classifiers
uint32_t n_cls_out = 1;
// llama4
// llama4 smallthinker
uint32_t n_moe_layer_step = 0;
uint32_t n_no_rope_layer_step = 4;
uint32_t n_attn_temp_floor_scale = 8192;
@@ -161,9 +161,10 @@ struct llama_hparams {
enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
// this value n_pattern means that every nth layer is dense (i.e. non-SWA)
// dense_first means whether the pattern is start with a dense layer
// note that if n_pattern == 0, all layers are SWA
// if n_pattern == 1, all layers are dense
// example: n_pattern = 3
// example 1: n_pattern = 3, dense_first = false
// il == 0: swa
// il == 1: swa
// il == 2: dense
@@ -172,7 +173,13 @@ struct llama_hparams {
// il == 5: dense
// il == 6: swa
// etc ...
void set_swa_pattern(uint32_t n_pattern);
// example 2: n_pattern = 2, dense_first = true
// il == 0: dense
// il == 1: swa
// il == 2: dense
// il == 3: swa
// etc ...
void set_swa_pattern(uint32_t n_pattern, bool dense_first = false);
// return true if one of the layers is SWA
bool is_swa_any() const;