model : Add LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M (#24913)
* model : Add LFM2.5-ColBERT-350M and LFM2.5-Embedding-350M * Restore LFM2 models in README.md
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+14
-4
@@ -190,7 +190,15 @@ llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_
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auto * conv_rs = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs);
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auto * conv = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs);
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bx = ggml_concat(ctx0, conv, bx, 0);
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// causal prepends the state, non-causal pads symmetrically for a centered window
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if (hparams.causal_attn) {
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bx = ggml_concat(ctx0, conv, bx, 0);
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} else {
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const int64_t pad = (hparams.n_shortconv_l_cache - 1) / 2;
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auto * left = ggml_cont(ctx0,
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ggml_view_3d(ctx0, conv, pad, hparams.n_embd, n_seqs, conv->nb[1], conv->nb[2], (d_conv - pad) * conv->nb[0]));
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bx = ggml_pad_ext(ctx0, ggml_concat(ctx0, left, bx, 0), 0, pad, 0, 0, 0, 0, 0, 0);
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}
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GGML_ASSERT(bx->ne[0] > conv->ne[0]);
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// last d_conv columns is a new conv state
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@@ -266,10 +274,12 @@ llama_model_lfm2::graph<iswa>::graph(const llama_model & model, const llm_graph_
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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cur = build_lora_mm(model.output, cur, model.output_s);
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cb(cur, "result_output", -1);
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if (!cparams.embeddings) {
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cur = build_lora_mm(model.output, cur, model.output_s);
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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res->t_logits = cur;
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}
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ggml_build_forward_expand(gf, cur);
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}
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