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model : add Jina Embeddings v5 Nano (partial EuroBERT) support (#19826)

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* WIP: Add EuroBERT support with autoformatting changes

This commit includes:
- EuroBERT model implementation for GGUF conversion
- C++ backend support for EuroBERT architecture
- Unintended autoformatting changes to Python files

Saving before reverting formatting-only changes.

* feat: add back eos assert when not last token pooling

* feat: removed duplicated code and cleanup

* feat: removed not working architectures and unnecessary check

* fix: typo

* fix: dynamic pooling config

* feat: added an example model for eurobert

* feat: proper llama-vocab implementation for jina-v5

* fix: removed unnecessary comments
This commit is contained in:
Maximilian Werk 2026-02-26 12:14:09 +01:00 • committed by GitHub
parent 1ca3d1de15
commit 66287bdaac
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12 changed files with 214 additions and 4 deletions
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39
src/llama-model.cpp
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@ -979,6 +979,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
type = LLM_TYPE_250M;
}
} break;
case LLM_ARCH_EUROBERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
if (hparams.n_layer == 12) {
type = LLM_TYPE_SMALL; // 0.2B
}
} break;
case LLM_ARCH_BLOOM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@ -3570,6 +3580,29 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
}
} break;
case LLM_ARCH_EUROBERT:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[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}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, 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);
}
} break;
case LLM_ARCH_JINA_BERT_V2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // word_embeddings
@ -8181,6 +8214,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_EUROBERT:
case LLM_ARCH_WAVTOKENIZER_DEC:
case LLM_ARCH_MODERN_BERT:
case LLM_ARCH_GEMMA_EMBEDDING:
@ -8378,6 +8412,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_neo_bert>(*this, params);
} break;
case LLM_ARCH_EUROBERT:
{
llm = std::make_unique<llm_build_eurobert>(*this, params);
} break;
case LLM_ARCH_BLOOM:
{
llm = std::make_unique<llm_build_bloom>(*this, params);
@ -9004,6 +9042,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_MODERN_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_EUROBERT:
case LLM_ARCH_STABLELM:
case LLM_ARCH_BITNET:
case LLM_ARCH_QWEN: