Issue with Loading a SLIM Model from a local directory

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I am experiencing an issue when attempting to load the `slim-sentiment-tool` model using the `load_model` method in the `ModelCatalog` class. The method raises a `ModelNotFoundException`, indicating that it cannot identify the model card for the selected model.

#### Steps to Reproduce
1. Download the model files and place them in the directory `/models/llmware/slim-sentiment-tool`.
```python
from llmware.models import pull_model_from_hf, ModelCatalog
model_card = ModelCatalog().lookup_model_card("slim-sentiment-tool")
pull_model_from_hf(model_card, "/models/llmware/slim-sentiment-tool")
```
2. Attempt to load the model using the following code:
```python
from llmware.models import ModelCatalog

def analyse_sentiment(text):
slim_model = ModelCatalog().load_model("/models/llmware/slim-sentiment-tool")
response = slim_model.function_call(text, get_logits=True)
analysis = ModelCatalog().logit_analysis(response, slim_model.model_card, slim_model.hf_tokenizer_name)
llm_response = response['llm_response']
confidence_score = float(analysis['confidence_score'])
return llm_response, confidence_score

text = "I am happy"
llm_response, confidence_score = analyse_sentiment(text)
```
3. Observe the following error:
```
ModelNotFoundException: '/models/llmware/slim-sentiment-tool' could not be located
```

#### Expected Behavior
The `load_model` method should correctly identify and load the model card for the `slim-sentiment-tool` model.

#### Actual Behavior
The method raises a `ModelNotFoundException`, indicating that it cannot identify the model card for the selected model.

#### Environment
- llmware version: 0.3.3
- Python version: 3.11

#### Request for Assistance
Could you please assist in identifying why the `load_model` method is unable to find the model card from the local path and suggest any necessary changes to fix this issue?

Thank you!

Start at ModelCatalog.load_model and compare its local-path handling with the pull_model_from_hf and lookup_model_card calls shown in the report. Reproduce the ModelNotFoundException with the downloaded directory; done means load_model identifies the local model card and the sentiment function_call example completes successfully.

Written by the indexing model from the issue text.