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Add config parameters for traced models (#3456) 

* Add config parameters for traced models

Signed-off-by: Naarcha-AWS <naarcha@amazon.com>

* Update model-serving-framework.md

* Update model-serving-framework.md

* Update model-serving-framework.md

* Update model-serving-framework.md

---------

Signed-off-by: Naarcha-AWS <naarcha@amazon.com>
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_ml-commons-plugin/model-serving-framework.md
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@ -53,20 +53,27 @@ The URL upload method requires the following request fields.
Field | Data type | Description
:--- | :--- | :---
`name`| string | The name of the model. |
`version` | string | The version number of the model. Since OpenSearch does not enforce a specific version schema for models, you can choose any number or format that makes sense for your models. |
`model_format` | string | The portable format of the model file. Currently only supports `TORCH_SCRIPT`. |
[`model_config`](#the-model_config-object) | json object | The model's configuration, including the `model_type`, `embedding_dimension`, and `framework_type`. |
`name`| String | The name of the model. |
`version` | String | The version number of the model. Since OpenSearch does not enforce a specific version schema for models, you can choose any number or format that makes sense for your models. |
`model_format` | String | The portable format of the model file. Currently only supports `TORCH_SCRIPT`. |
[`model_config`](#the-model_config-object) | JSON object | The model's configuration, including the `model_type`, `embedding_dimension`, and `framework_type`. |
`url` | string | The URL where the model is located. |
### The `model_config` object
| Field | Data type | Description |
| :--- | :--- | :--- |
| `model_type` | string | The model type, such as `bert`. For a Huggingface model, the model type is specified in `config.json`. For an example, see the [`all-MiniLM-L6-v2` Huggingface model `config.json`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/blob/main/config.json#L15).|
| `embedding_dimension` | integer | The dimension of the model-generated dense vector. For a Huggingface model, the dimension is specified in the model card. For example, in the [`all-MiniLM-L6-v2` Huggingface model card](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2), the statement `384 dimensional dense vector space` specifies 384 as the embedding dimension. |
| `framework_type` | string | The framework the model is using. Currently, we support `sentence_transformers` and `huggingface_transformers` frameworks. The `sentence_transformers` model outputs text embeddings directly, so ML Commons does not perform any post processing. For `huggingface_transformers`, ML Commons performs post processing by applying mean pooling to get text embeddings. See the example [`all-MiniLM-L6-v2` Huggingface model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for more details. |
| `all_config` _(Optional)_ | string | This field is used for reference purposes. You can specify all model configurations in this field. For example, if you are using a Huggingface model, you can minify the `config.json` file to one line and save its contents in the `all_config` field. Once the model is uploaded, you can use the get model API operation to get all model configurations stored in this field. |
| `model_type` | String | The model type, such as `bert`. For a Huggingface model, the model type is specified in `config.json`. For an example, see the [`all-MiniLM-L6-v2` Huggingface model `config.json`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/blob/main/config.json#L15).|
| `embedding_dimension` | Integer | The dimension of the model-generated dense vector. For a Huggingface model, the dimension is specified in the model card. For example, in the [`all-MiniLM-L6-v2` Huggingface model card](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2), the statement `384 dimensional dense vector space` specifies 384 as the embedding dimension. |
| `framework_type` | String | The framework the model is using. Currently, we support `sentence_transformers` and `huggingface_transformers` frameworks. The `sentence_transformers` model outputs text embeddings directly, so ML Commons does not perform any post processing. For `huggingface_transformers`, ML Commons performs post processing by applying mean pooling to get text embeddings. See the example [`all-MiniLM-L6-v2` Huggingface model](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for more details. |
| `all_config` _(Optional)_ | String | This field is used for reference purposes. You can specify all model configurations in this field. For example, if you are using a Huggingface model, you can minify the `config.json` file to one line and save its contents in the `all_config` field. Once the model is uploaded, you can use the get model API operation to get all model configurations stored in this field. |
You can further customize a pre-trained sentence transformer model's post-processing logic with the following optional fields in the `model_config` object.
| Field | Data type | Description |
| :--- | :--- | :--- |
| `pooling_mode` | String | The post-process model output, either `mean`, `mean_sqrt_len`, `max`, `weightedmean`, or `cls`.|
| `normalize_result` | Boolean | When set to `true`, normalizes the model output in order to scale to a standard range for the model. |
#### Example request