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model: support embeddinggemma2 (text+vision+audio)

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This commit is contained in:
Xuan Son Nguyen
2026-10-06 17:59:06 +02:00
parent a46709b683
commit 1e91a0b054
10 changed files with 324 additions and 2 deletions
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conversion/__init__.py
+2
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@@ -74,6 +74,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
"Dots3NoteTextForCausalLM": "dots3",
"DotsOCRForCausalLM": "qwen",
"DreamModel": "dream",
"EmbeddingGemma2Model": "gemma",
"Ernie4_5ForCausalLM": "ernie",
"Ernie4_5_ForCausalLM": "ernie",
"Ernie4_5_MoeForCausalLM": "ernie",
@@ -308,6 +309,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
"Dots3NoteForCausalLM": "dots3",
"Dots3NoteForConditionalGeneration": "dots3",
"DotsOCRForCausalLM": "dotsocr",
"EmbeddingGemma2Model": "gemma",
"Exaone4_5_ForConditionalGeneration": "exaone",
"Gemma3ForConditionalGeneration": "gemma",
"Gemma3nForConditionalGeneration": "gemma",
conversion/gemma.py
+31 -1
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@@ -700,7 +700,7 @@ class Gemma4Model(Gemma3Model):
self.gguf_writer.add_key_length_swa(head_dim_swa)
self.gguf_writer.add_value_length_swa(head_dim_swa)
expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"])
expert_intermediate_size = self.find_hparam(["expert_intermediate_size", "moe_intermediate_size"], optional=True)
if expert_intermediate_size is not None:
self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
@@ -810,6 +810,28 @@ class Gemma4Model(Gemma3Model):
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("EmbeddingGemma2Model")
# TODO: add example model
class EmbeddingGemma2Model(Gemma4Model):
model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING2
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.hparams["num_kv_shared_layers"] = 0
def set_gguf_parameters(self):
super().set_gguf_parameters()
# HF sliding_window is bidirectional, llama.cpp expects the full window size
self.gguf_writer.add_sliding_window(2 * self.hparams["sliding_window"])
self.gguf_writer.add_embedding_length_out(self.hparams["embedding_dim"])
self.gguf_writer.add_causal_attention(False)
self._try_set_pooling_type()
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
# default rope on all layers, no rope_freqs needed
return iter(())
@ModelBase.register("Gemma4DSparkModel")
class Gemma4DSparkModel(DFlashModel):
model_arch = gguf.MODEL_ARCH.DFLASH
@@ -1030,6 +1052,14 @@ class Gemma4VisionAudioModel(MmprojModel):
yield (mapped_name, data_torch)
@ModelBase.register("EmbeddingGemma2Model")
# TODO: add example model
class EmbeddingGemma2VisionAudioModel(Gemma4VisionAudioModel):
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# same towers as Gemma4, but the tensor names have no "model." prefix
yield from super().modify_tensors(data_torch, "model." + name, bid)
@ModelBase.register("Gemma4UnifiedForConditionalGeneration")
@ModelBase.example("hf-tiny-v2/tiny-random-Gemma4UnifiedForConditionalGeneration")
class Gemma4UnifiedVisionAudioModel(Gemma4VisionAudioModel):
gguf-py/gguf/constants.py
+26
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@@ -559,6 +559,7 @@ class MODEL_ARCH(IntEnum):
GEMMA4 = auto()
GEMMA4_ASSISTANT = auto()
GEMMA_EMBEDDING = auto()
GEMMA_EMBEDDING2 = auto()
STARCODER2 = auto()
RWKV6 = auto()
RWKV6QWEN2 = auto()
@@ -1338,6 +1339,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.GEMMA4: "gemma4",
MODEL_ARCH.GEMMA4_ASSISTANT: "gemma4-assistant",
MODEL_ARCH.GEMMA_EMBEDDING: "gemma-embedding",
MODEL_ARCH.GEMMA_EMBEDDING2: "gemma-embedding2",
MODEL_ARCH.STARCODER2: "starcoder2",
MODEL_ARCH.RWKV6: "rwkv6",
MODEL_ARCH.RWKV6QWEN2: "rwkv6qwen2",
@@ -3471,6 +3473,30 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_PRE_NORM,
MODEL_TENSOR.FFN_POST_NORM,
],
MODEL_ARCH.GEMMA_EMBEDDING2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_POST_NORM,
MODEL_TENSOR.FFN_PRE_NORM,
MODEL_TENSOR.FFN_POST_NORM,
MODEL_TENSOR.LAYER_OUT_SCALE,
MODEL_TENSOR.PER_LAYER_MODEL_PROJ,
MODEL_TENSOR.PER_LAYER_INP_GATE,
MODEL_TENSOR.PER_LAYER_PROJ,
MODEL_TENSOR.PER_LAYER_PROJ_NORM,
MODEL_TENSOR.PER_LAYER_POST_NORM,
],
MODEL_ARCH.STARCODER2: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
gguf-py/gguf/tensor_mapping.py
+7
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@@ -88,6 +88,7 @@ class TensorNameMap:
"model.lm_head", # dflash
"model.transformer.ff_out", # llada
"head.decoder", # modern-bert
"embedding_projection", # embeddinggemma2
),
MODEL_TENSOR.DENSE_2_OUT: (
"dense_2_out", # embeddinggemma
@@ -753,6 +754,7 @@ class TensorNameMap:
MODEL_TENSOR.LAYER_OUT_SCALE: (
"model.layers.{bid}.layer_scalar", # gemma4
"layers.{bid}.layer_scalar", # embeddinggemma2
"model.blocks.{bid}.embed_skip.a_g", # talkie
),
@@ -762,10 +764,12 @@ class TensorNameMap:
MODEL_TENSOR.PER_LAYER_MODEL_PROJ: (
"model.per_layer_model_projection", # gemma3n
"ple.per_layer_model_projection", # embeddinggemma2
),
MODEL_TENSOR.PER_LAYER_PROJ_NORM: (
"model.per_layer_projection_norm", # gemma3n
"ple.per_layer_projection_norm", # embeddinggemma2
),
MODEL_TENSOR.ALTUP_PROJ: (
@@ -778,14 +782,17 @@ class TensorNameMap:
MODEL_TENSOR.PER_LAYER_INP_GATE: (
"model.layers.{bid}.per_layer_input_gate", # gemma3n
"layers.{bid}.ple_block.per_layer_input_gate", # embeddinggemma2
),
MODEL_TENSOR.PER_LAYER_PROJ: (
"model.layers.{bid}.per_layer_projection", # gemma3n
"layers.{bid}.ple_block.per_layer_projection", # embeddinggemma2
),
MODEL_TENSOR.PER_LAYER_POST_NORM: (
"model.layers.{bid}.post_per_layer_input_norm", # gemma3n
"layers.{bid}.ple_block.post_per_layer_input_norm", # embeddinggemma2
),
MODEL_TENSOR.ALTUP_CORRECT_COEF: (
src/llama-arch.cpp
+1
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@@ -60,6 +60,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GEMMA4, "gemma4" },
{ LLM_ARCH_GEMMA4_ASSISTANT, "gemma4-assistant" },
{ LLM_ARCH_GEMMA_EMBEDDING, "gemma-embedding" },
{ LLM_ARCH_GEMMA_EMBEDDING2, "gemma-embedding2" },
{ LLM_ARCH_STARCODER2, "starcoder2" },
{ LLM_ARCH_MAMBA, "mamba" },
{ LLM_ARCH_MAMBA2, "mamba2" },
src/llama-arch.h
+1
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@@ -65,6 +65,7 @@ enum llm_arch {
LLM_ARCH_GEMMA4,
LLM_ARCH_GEMMA4_ASSISTANT,
LLM_ARCH_GEMMA_EMBEDDING,
LLM_ARCH_GEMMA_EMBEDDING2,
LLM_ARCH_STARCODER2,
LLM_ARCH_MAMBA,
LLM_ARCH_MAMBA2,
src/llama-model.cpp
+4
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@@ -156,6 +156,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_gemma4_assistant(params);
case LLM_ARCH_GEMMA_EMBEDDING:
return new llama_model_gemma_embedding(params);
case LLM_ARCH_GEMMA_EMBEDDING2:
return new llama_model_gemma_embedding2(params);
case LLM_ARCH_STARCODER2:
return new llama_model_starcoder2(params);
case LLM_ARCH_MAMBA:
@@ -2371,6 +2373,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
case LLM_ARCH_WAVTOKENIZER_DEC:
case LLM_ARCH_MODERN_BERT:
case LLM_ARCH_GEMMA_EMBEDDING:
case LLM_ARCH_GEMMA_EMBEDDING2:
case LLM_ARCH_DREAM:
case LLM_ARCH_LLADA:
case LLM_ARCH_LLADA_MOE:
@@ -3152,6 +3155,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_GEMMA4:
case LLM_ARCH_GEMMA4_ASSISTANT:
case LLM_ARCH_GEMMA_EMBEDDING:
case LLM_ARCH_GEMMA_EMBEDDING2:
case LLM_ARCH_STARCODER2:
case LLM_ARCH_OPENELM:
case LLM_ARCH_GPTNEOX:
src/models/gemma-embedding2.cpp
+234
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@@ -0,0 +1,234 @@
#include "models.h"
void llama_model_gemma_embedding2::load_arch_hparams(llama_model_loader & ml) {
hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
load_swa_pattern(ml, 6);
hparams.causal_attn = false; // embeddings do not use causal attention
hparams.f_attention_scale = 1.0f; // same as Gemma4, q_norm makes the scaling unnecessary
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, hparams.n_embd_per_layer);
switch (hparams.n_layer()) {
case 24: type = LLM_TYPE_0_3B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_gemma_embedding2::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_embd_per_layer = hparams.n_embd_per_layer;
const int64_t n_embd_out = hparams.n_embd_out();
if (n_embd_head_k != n_embd_head_v) {
throw std::runtime_error("EmbeddingGemma2 requires n_embd_head_k == n_embd_head_v");
}
if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) {
throw std::runtime_error("EmbeddingGemma2 requires n_embd_head_k_swa == n_embd_head_v_swa");
}
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_per_layer * n_layer}, 0);
per_layer_proj_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM, "weight", 0), {n_embd_per_layer}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
// projects the final hidden state to the embedding dimension
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_embd_out}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
const int64_t n_head = hparams.n_head(i);
const int64_t n_embd_head = hparams.n_embd_head_k(i);
const int64_t n_embd_k = hparams.n_embd_k_gqa(i);
const int64_t n_embd_v = hparams.n_embd_v_gqa(i);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0);
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head}, 0);
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head}, 0);
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
layer.per_layer_inp_gate = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE, "weight", i), {n_embd, n_embd_per_layer}, 0);
layer.per_layer_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ, "weight", i), {n_embd_per_layer, n_embd}, 0);
layer.per_layer_post_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0);
layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1u}, 0);
}
}
std::unique_ptr<llm_graph_context> llama_model_gemma_embedding2::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
llama_model_gemma_embedding2::graph::graph(const llama_model & model, const llm_graph_params & params) :
llm_graph_context(params),
model(model) {
ggml_tensor * cur;
ggml_tensor * inpL;
// important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings)
inpL = build_inp_embd(model.tok_embd, sqrtf(n_embd));
cb(inpL, "inp_scaled", -1);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_no_cache();
ggml_tensor * inp_out_ids = build_inp_out_ids();
// inp_per_layer shape: [n_embd_per_layer, n_tokens, n_layer]
ggml_tensor * inp_per_layer = build_inp_per_layer(inpL);
for (int il = 0; il < n_layer; ++il) {
const int64_t n_embd_head = hparams.n_embd_head_k(il);
const int64_t n_head = hparams.n_head(il);
const int64_t n_head_kv = hparams.n_head_kv(il);
const float freq_base_l = model.get_rope_freq_base(cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
const int n_rot_l = hparams.n_rot(il);
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);
cb(Qcur, "Qcur_normed", il);
cb(Kcur, "Kcur_normed", il);
cb(Vcur, "Vcur_normed", il);
Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
ext_factor, attn_factor, beta_fast, beta_slow);
cb(Qcur, "Qcur_pos", il);
cb(Kcur, "Kcur_pos", il);
cur = build_attn(inp_attn,
model.layers[il].wo, nullptr, model.layers[il].wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
cur = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_post_norm", il);
ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL);
cb(attn_out, "attn_out", il);
// feed-forward network
cur = build_norm(attn_out, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, nullptr, model.layers[il].ffn_up_s,
model.layers[il].ffn_gate, nullptr, model.layers[il].ffn_gate_s,
model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s,
nullptr,
LLM_FFN_GELU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
cur = build_norm(cur, model.layers[il].ffn_post_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "ffn_post_norm", il);
cur = ggml_add(ctx0, cur, attn_out);
// per-layer embedding
{
ggml_tensor * pe_in = cur;
cb(cur, "pe_in", il);
cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens]
cur = ggml_gelu(ctx0, cur);
ggml_tensor * inp_this_layer = ggml_view_2d(ctx0, inp_per_layer,
inp_per_layer->ne[0], inp_per_layer->ne[1], inp_per_layer->nb[1], il * inp_per_layer->nb[2]);
if (il == n_layer - 1 && inp_out_ids) {
inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids);
}
cur = ggml_mul(ctx0, cur, inp_this_layer);
cur = build_lora_mm(model.layers[il].per_layer_proj, cur); // [n_embd, n_tokens]
cur = build_norm(cur, model.layers[il].per_layer_post_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "per_layer_embd_out", il);
cur = ggml_add(ctx0, pe_in, cur);
}
// layer_scalar
cur = ggml_mul(ctx0, cur, model.layers[il].out_scale);
cb(cur, "out_scaled", il);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = build_norm(inpL, model.output_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
// projecting per token is equivalent to projecting after mean pooling
cur = build_lora_mm(model.output, cur, model.output_s);
cb(cur, "result_embd", -1);
res->t_embd = cur;
ggml_build_forward_expand(gf, cur);
}
// equivalent to EmbeddingGemma2TextPLE in python code
// this model has no per-layer token embeddings, the per-layer inputs come only from the projection
// inpL shape: [n_embd, n_tokens]
// output shape: [n_embd_per_layer, n_tokens, n_layer]
ggml_tensor * llama_model_gemma_embedding2::graph::build_inp_per_layer(ggml_tensor * inpL) {
const int64_t n_embd_per_layer = hparams.n_embd_per_layer;
ggml_tensor * cur = ggml_mul_mat(ctx0, model.per_layer_model_proj, inpL); // [n_embd_per_layer * n_layer, n_tokens]
cur = ggml_scale(ctx0, cur, 1.0f / sqrtf((float) n_embd));
cur = ggml_reshape_3d(ctx0, cur, n_embd_per_layer, n_layer, n_tokens);
cur = build_norm(cur, model.per_layer_proj_norm, nullptr, LLM_NORM_RMS, -1);
cb(cur, "inp_per_layer", -1);
// permute to shape: [n_embd_per_layer, n_tokens, n_layer]
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3));
return cur;
}
src/models/models.h
+17
View File
@@ -914,6 +914,23 @@ struct llama_model_gemma_embedding : public llama_model_base {
};
struct llama_model_gemma_embedding2 : public llama_model_base {
llama_model_gemma_embedding2(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
struct graph : public llm_graph_context {
const llama_model & model;
graph(const llama_model & model, const llm_graph_params & params);
ggml_tensor * build_inp_per_layer(ggml_tensor * inpL);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_starcoder2 : public llama_model_base {
llama_model_starcoder2(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
tests/test-llama-archs.cpp
+1 -1
View File
@@ -729,7 +729,7 @@ static bool arch_supported(const llm_arch arch) {
if (arch == LLM_ARCH_GRANITE_SWITCH) {
return false; // FIXME adapter fixture
}
if (arch == LLM_ARCH_LLAMA_EMBED || arch == LLM_ARCH_GEMMA_EMBEDDING || arch == LLM_ARCH_T5ENCODER) {
if (arch == LLM_ARCH_LLAMA_EMBED || arch == LLM_ARCH_GEMMA_EMBEDDING || arch == LLM_ARCH_GEMMA_EMBEDDING2 || arch == LLM_ARCH_T5ENCODER) {
return false; // FIXME Embedding (?) models produce inconsistent results.
}
if (arch == LLM_ARCH_RWKV6 || arch == LLM_ARCH_RWKV6QWEN2 || arch == LLM_ARCH_RWKV7 || arch == LLM_ARCH_ARWKV7) {