{ "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD },
{ "granite", LLM_CHAT_TEMPLATE_GRANITE_3_X },
{ "granite-4.0", LLM_CHAT_TEMPLATE_GRANITE_4_0 },
+ { "granite-4.1", LLM_CHAT_TEMPLATE_GRANITE_4_1 },
{ "gigachat", LLM_CHAT_TEMPLATE_GIGACHAT },
{ "megrez", LLM_CHAT_TEMPLATE_MEGREZ },
{ "yandex", LLM_CHAT_TEMPLATE_YANDEX },
return LLM_CHAT_TEMPLATE_RWKV_WORLD;
} else if (tmpl_contains("<|start_of_role|>")) {
if (tmpl_contains("<tool_call>") || tmpl_contains("<tools>")) {
- return LLM_CHAT_TEMPLATE_GRANITE_4_0;
+ if (tmpl_contains("g4_default_system_message")) {
+ return LLM_CHAT_TEMPLATE_GRANITE_4_0;
+ }
+ return LLM_CHAT_TEMPLATE_GRANITE_4_1;
}
return LLM_CHAT_TEMPLATE_GRANITE_3_X;
} else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
if (add_ass) {
ss << "<|start_of_role|>assistant<|end_of_role|>";
}
+ } else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE_4_1) {
+ // IBM Granite 4.1 template
+ for (const auto & message : chat) {
+ std::string role(message->role);
+ if (role == "assistant_tool_call") {
+ ss << "<|start_of_role|>assistant<|end_of_role|><|tool_call|>";
+ } else {
+ ss << "<|start_of_role|>" << role << "<|end_of_role|>";
+ }
+ ss << message->content << "<|end_of_text|>\n";
+ }
+ if (add_ass) {
+ ss << "<|start_of_role|>assistant<|end_of_role|>";
+ }
} else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
// GigaChat template
bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";
--- /dev/null
+#include "models.h"
+
+void llama_model_talkie::load_arch_hparams(llama_model_loader & ml) {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+
+ switch (hparams.n_layer) {
+ case 40: type = LLM_TYPE_13B; break;
+ default: type = LLM_TYPE_UNKNOWN;
+ }
+}
+
+void llama_model_talkie::load_arch_tensors(llama_model_loader &) {
+ LLAMA_LOAD_LOCALS;
+
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
+
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = layers[i];
+
+ create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ // no k gain
+ layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {1, n_head}, 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.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1}, 0);
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_talkie::build_arch_graph(const llm_graph_params & params) const {
+ return std::make_unique<graph>(*this, params);
+}
+
+llama_model_talkie::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+ const int64_t n_embd_head = hparams.n_embd_head_k();
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_v());
+ GGML_ASSERT(n_embd_head == n_rot);
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ inpL = build_inp_embd(model.tok_embd);
+ inpL = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, -1);
+ cb(inpL, "inp_norm", -1);
+
+ ggml_tensor * embd_skip = inpL;
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv();
+
+ ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+ const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+ for (int il = 0; il < n_layer; ++il) {
+ ggml_tensor * inpSA = inpL;
+ ggml_tensor * inp_skip = embd_skip;
+
+ cur = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
+ n_embd_head, n_head, n_head_kv, il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+
+ // reference applies qknorm after rope
+ Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
+ cb(Qcur, "Qcur_norm", il);
+
+ Kcur = build_norm(Kcur, nullptr, nullptr, LLM_NORM_RMS, il);
+ cb(Kcur, "Kcur_norm", il);
+
+ cb(Vcur, "Vcur", il);
+
+ cur = build_attn(inp_attn,
+ model.layers[il].wo, nullptr, model.layers[il].wo_s,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+ cb(cur, "attn_out", il);
+ }
+
+ if (il == n_layer - 1 && inp_out_ids) {
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ inp_skip = ggml_get_rows(ctx0, inp_skip, inp_out_ids);
+ }
+
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ cur = build_norm(ffn_inp, nullptr, nullptr, LLM_NORM_RMS, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = build_ffn(cur,
+ model.layers[il].ffn_up, nullptr, nullptr,
+ model.layers[il].ffn_gate, nullptr, nullptr,
+ model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s,
+ nullptr,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+
+ ggml_tensor * skip = ggml_mul(ctx0, inp_skip, model.layers[il].out_scale);
+ cb(skip, "embd_skip", il);
+
+ cur = ggml_add(ctx0, cur, skip);
+
+ cur = build_cvec(cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = build_norm(cur, nullptr, nullptr, LLM_NORM_RMS, -1);
+ cb(cur, "result_norm", -1);
+
+ res->t_embd = cur;
+
+ cur = build_lora_mm(model.output, cur);
+ cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
+ cb(cur, "result_output", -1);
+
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
overview / pkg / ggml / sources / whisper.cpp / commitdiff