[Docs] Clarify get-events API usage

docs/guides/fine_tuning.md

@@ -141,8 +141,8 @@ from llmengine import FineTune
 
 response = FineTune.create(
     model="llama-2-7b",
-    training_file="file-7DLVeLdN2Ty4M2m",
-    validation_file="file-ezSRtpgKQyItI26",
+    training_file="file-AbCDeLdN2Ty4M2m",
+    validation_file="file-ezSRpgtKQyItI26",
 )
 
 print(response.json())
@@ -152,7 +152,35 @@ See the [Model Zoo](../../model_zoo) to see which models have fine-tuning suppor
 
 See [Integrations](../integrations.md) to see how to track fine-tuning metrics.
 
-Once the fine-tune is launched, you can also [get the status of your fine-tune](../../api/python_client/#llmengine.fine_tuning.FineTune.get). You can also [list events that your fine-tune produces](../../api/python_client/#llmengine.fine_tuning.FineTune.get_events).
+## Monitoring the fine-tune
+
+Once the fine-tune is launched, you can also [get the status of your fine-tune](../../api/python_client/#llmengine.fine_tuning.FineTune.get). 
+You can also [list events that your fine-tune produces](../../api/python_client/#llmengine.fine_tuning.FineTune.get_events).
+```python
+from llmengine import FineTune
+
+fine_tune_id = "ft-cabcdefghi1234567890"
+fine_tune = FineTune.get(fine_tune_id)
+print(fine_tune.status)  # BatchJobStatus.RUNNING
+print(fine_tune.fine_tuned_model)  # "llama-2-7b.700101-000000
+
+fine_tune_events = FineTune.get_events(fine_tune_id)
+for event in fine_tune_events.events:
+    print(event)
+# Prints something like:
+# timestamp=1697590000.0 message="{'loss': 12.345, 'learning_rate': 0.0, 'epoch': 0.97}" level='info'
+# timestamp=1697590000.0 message="{'eval_loss': 23.456, 'eval_runtime': 19.876, 'eval_samples_per_second': 4.9, 'eval_steps_per_second': 4.9, 'epoch': 0.97}" level='info'
+# timestamp=1697590020.0 message="{'train_runtime': 421.234, 'train_samples_per_second': 2.042, 'train_steps_per_second': 0.042, 'total_flos': 123.45, 'train_loss': 34.567, 'epoch': 0.97}" level='info'
+
+
+```
+
+The status of your fine-tune will give a high-level overview of the fine-tune's progress.
+The events of your fine-tune will give more detail, such as the training loss and validation loss at each epoch, 
+as well as any errors that may have occurred. If you encounter any errors with your fine-tune, 
+the events are a good place to start debugging. For example, if you see `Unable to read training or validation dataset`,
+you may need to make your files accessible to LLM Engine. If you see `Invalid value received for lora parameter 'lora_alpha'!`,
+you should [check that your hyperparameters are valid](../../api/python_client/#llmengine.fine_tuning.FineTune.create).
 
 ## Making inference calls to your fine-tune
 

docs/model_zoo.md

@@ -18,8 +18,8 @@ Scale hosts the following models in the LLM Engine Model Zoo:
 | `mpt-7b`              | ✅                       |                            | deepspeed, text-generation-inference, vllm |
 | `mpt-7b-instruct`     | ✅                       | ✅                         | deepspeed, text-generation-inference, vllm |
 | `flan-t5-xxl`         | ✅                       |                            | deepspeed, text-generation-inference |
-| `mistral-7b`         | ✅                       |                            | vllm | 
-| `mistral-7b-instruct`         | ✅                       |                            | vllm |
+| `mistral-7b`         | ✅                       |   ✅                         | vllm | 
+| `mistral-7b-instruct`         | ✅                       |    ✅                        | vllm |
 
 ## Usage