[Docs] Add Giga-Embeddings bidirectional Qwen3 usage to embed docs (#60854)

Signed-off-by: Abhijit <abroy@redhat.com>

docs/models/pooling_models/embed.md

@@ -50,7 +50,7 @@ You can compute pairwise similarity scores to build a similarity matrix using th
 | `ModernBertModel` | ModernBERT-based | `Alibaba-NLP/gte-modernbert-base`, etc. | ✅︎ | |
 | `NomicBertModel` | Nomic BERT | `nomic-ai/nomic-embed-text-v1`, `nomic-ai/nomic-embed-text-v2-moe`, `Snowflake/snowflake-arctic-embed-m-long`, etc. | | |
 | `Qwen2Model`<sup>C</sup>, `Qwen2ForCausalLM`<sup>C</sup> | Qwen2-based | `ssmits/Qwen2-7B-Instruct-embed-base` (see note), `Alibaba-NLP/gte-Qwen2-7B-instruct` (see note), etc. | ✅︎ | ✅︎ |
-| `Qwen3Model`<sup>C</sup>, `Qwen3ForCausalLM`<sup>C</sup> | Qwen3-based | `Qwen/Qwen3-Embedding-0.6B`, etc. | ✅︎ | ✅︎ |
+| `Qwen3Model`<sup>C</sup>, `Qwen3ForCausalLM`<sup>C</sup> | Qwen3-based | `Qwen/Qwen3-Embedding-0.6B`, `ai-sage/Giga-Embeddings-instruct-480M-0826`<sup>^</sup>, etc. | ✅︎ | ✅︎ |
 | `RobertaModel`, `RobertaForMaskedLM` | RoBERTa-based | `sentence-transformers/all-roberta-large-v1`, etc. | | |
 | `VoyageQwen3BidirectionalEmbedModel`<sup>C</sup> | Voyage Qwen3-based with bidirectional attention | `voyageai/voyage-4-nano`, etc. | ✅︎ | ✅︎ |
 | `XLMRobertaModel` | XLMRobertaModel-based | `BAAI/bge-m3` (see note), `intfloat/multilingual-e5-base`, `jinaai/jina-embeddings-v3` (see note), etc. | | |
@@ -97,6 +97,7 @@ You can compute pairwise similarity scores to build a similarity matrix using th
 | `*ForConditionalGeneration`<sup>C</sup>, `*ForCausalLM`<sup>C</sup>, etc. | Generative models | \* | N/A | \* | \* |
 
 <sup>C</sup> Automatically converted into an embedding model via `--convert embed`. ([details](./README.md#model-conversion))  
+<sup>^</sup> You need to set the architecture name via `--hf-overrides` to match the one in vLLM.  
 \* Feature support is the same as that of the original model.
 
 If your model is not in the above list, we will try to automatically convert the model using