model : Add LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M

README.md

@@ -142,7 +142,9 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
 - [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview)
 - [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
-- [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38)
+- [x] [Liquid LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2)
+- [x] [Liquid LFM2.5 models](https://huggingface.co/collections/LiquidAI/lfm25)
+- [x] [Liquid Nanos](https://huggingface.co/collections/LiquidAI/liquid-nanos)
 - [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
 - [x] [BailingMoeV2 (Ring/Ling 2.0) models](https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
 - [x] [Mellum models](https://huggingface.co/JetBrains/models?search=mellum)

conversion/__init__.py

@@ -123,6 +123,7 @@
     "LLaDAModelLM": "llada",
     "LLaMAForCausalLM": "llama",
     "Lfm25AudioTokenizer": "lfm2",
+    "Lfm2BidirectionalModel": "lfm2",
     "Lfm2ForCausalLM": "lfm2",
     "Lfm2Model": "lfm2",
     "Lfm2MoeForCausalLM": "lfm2",

conversion/lfm2.py

@@ -64,22 +64,29 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Lfm2Model")
+@ModelBase.register("Lfm2Model", "Lfm2BidirectionalModel")
 class LFM2ColBertModel(LFM2Model):
     model_arch = gguf.MODEL_ARCH.LFM2
     dense_tensor_name = "dense_2"
 
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.hf_arch == "Lfm2BidirectionalModel":
+            self.gguf_writer.add_causal_attention(False)
+        self._try_set_pooling_type()
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if not name.startswith(self.dense_tensor_name):
             name = "model." + name
 
         yield from super().modify_tensors(data_torch, name, bid)
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        # dense tensor is stored in a separate safetensors file
+        # optional dense tensor is stored in a separate safetensors file
         from safetensors.torch import load_file
         tensors_file = self.dir_model / "1_Dense" / "model.safetensors"
-        assert tensors_file.is_file()
+        if not tensors_file.is_file():
+            return
         tensor = load_file(tensors_file)["linear.weight"]
         self.gguf_writer.add_embedding_length_out(tensor.shape[0])
         yield f"{self.dense_tensor_name}.weight", tensor.clone()

src/models/lfm2.cpp

@@ -190,7 +190,15 @@ llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_
         auto * conv_rs    = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs);
         auto * conv       = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs);
 
-        bx = ggml_concat(ctx0, conv, bx, 0);
+        // causal prepends the state, non-causal pads symmetrically for a centered window
+        if (hparams.causal_attn) {
+            bx = ggml_concat(ctx0, conv, bx, 0);
+        } else {
+            const int64_t pad = (hparams.n_shortconv_l_cache - 1) / 2;
+            auto * left = ggml_cont(ctx0,
+                ggml_view_3d(ctx0, conv, pad, hparams.n_embd, n_seqs, conv->nb[1], conv->nb[2], (d_conv - pad) * conv->nb[0]));
+            bx = ggml_pad_ext(ctx0, ggml_concat(ctx0, left, bx, 0), 0, pad, 0, 0, 0, 0, 0, 0);
+        }
         GGML_ASSERT(bx->ne[0] > conv->ne[0]);
 
         // last d_conv columns is a new conv state
@@ -266,10 +274,12 @@ llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_
     cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
-    cur = build_lora_mm(model.output, cur, model.output_s);
-    cb(cur, "result_output", -1);
+    if (!cparams.embeddings) {
+        cur = build_lora_mm(model.output, cur, model.output_s);
+        cb(cur, "result_output", -1);
 
-    res->t_logits = cur;
+        res->t_logits = cur;
+    }
 
     ggml_build_forward_expand(gf, cur);
 }