#include <map>
static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+ { LLM_ARCH_CLIP, "clip" }, // dummy, only used by llama-quantize
{ LLM_ARCH_LLAMA, "llama" },
{ LLM_ARCH_LLAMA4, "llama4" },
{ LLM_ARCH_DECI, "deci" },
};
static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_NAMES = {
+ {
+ LLM_ARCH_CLIP,
+ {},
+ },
{
LLM_ARCH_LLAMA,
{
//
enum llm_arch {
+ LLM_ARCH_CLIP,
LLM_ARCH_LLAMA,
LLM_ARCH_LLAMA4,
LLM_ARCH_DECI,
ml.get_key(LLM_KV_GENERAL_NAME, name, false);
// everything past this point is not vocab-related
- if (hparams.vocab_only) {
+ // for CLIP models, we only need to load tensors, no hparams
+ if (hparams.vocab_only || ml.get_arch() == LLM_ARCH_CLIP) {
return;
}
llama_rope_type llama_model_rope_type(const llama_model * model) {
switch (model->arch) {
// these models do not use RoPE
+ case LLM_ARCH_CLIP:
case LLM_ARCH_GPT2:
case LLM_ARCH_GPTJ:
case LLM_ARCH_MPT:
});
}
+ bool is_clip_model = false;
for (const auto * it : tensors) {
const struct ggml_tensor * tensor = it->tensor;
} else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
qs.has_output = true;
}
+
+ is_clip_model |= name.rfind("mm.", 0) == 0; // check the "mm." prefix
}
qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
// sanity checks for models that have attention layers
- if (qs.n_attention_wv != 0)
+ if (qs.n_attention_wv != 0 && !is_clip_model)
{
const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin();
// attention layers have a non-zero number of kv heads
// do not quantize relative position bias (T5)
quantize &= name.find("attn_rel_b.weight") == std::string::npos;
+ // do not quantize specific multimodal tensors
+ quantize &= name.find(".position_embd.") == std::string::npos;
+
ggml_type new_type;
void * new_data;
size_t new_size;
} catch(const std::exception & e) {
throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
}
+ if (model.arch == LLM_ARCH_CLIP) {
+ throw std::runtime_error("CLIP cannot be used as main model, use it with --mmproj instead");
+ }
try {
model.load_vocab(ml);
} catch(const std::exception & e) {